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An approach for fault detection and location in solar PV systems
Solar Energy 194 (2019) 197–208
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Solar Energy journal homepage: www.elsevier.com/locate/solener
An approach for fault detection and location in solar PV systems ⁎
T
Amit Dhoke, Rahul Sharma , Tapan Kumar Saha School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Fault detection and location Fault current PV protection PV system String monitoring
The task of fault detection and diagnosis in large-scale photovoltaic (PV) plants is expected to be a major challenge as more and more plants with increasingly large capacities continue to come into existence. To maintain safety, reliability, and productivity of large-scale PV plants it is essential to develop approaches that allow automatic detection and location of any mal-operation among thousands of PV modules. This paper proposes an approach to detect PV plant faults through the generation of fault indicator signals called ‘residuals’ for each string and the comparison of residuals with a threshold value. Furthermore, a regression-based approach is proposed to estimate fault location as a function of fault current and irradiance level measurements. The proposed approach is demonstrated by specifically focusing on intra-string line-line faults. Various line-line fault case studies are verified through simulations and validated on an experimental setup in a solar PV plant. Initially the approach is developed for a fixed size PV array, but subsequently extensions of the proposed approach to any array size are developed and presented. The generalisation potential of the approach is demonstrated through successful validation on multiple PV array configurations.
1. Introduction Solar photovoltaic (PV) growth is greatly increasing worldwide due to its technical, economical and environmental benefits. To meet the energy demand, currently, PV is the most promising source of sustainable power generation. With the growing energy demand, largescale PV installations are gaining more attention to meet the energy targets. Thousands of large-scale PV plants are in the commissioning phase to meet hundreds of giga-Watts of demand. A large-scale PV system comprises of a very large number (hundreds of thousands) of solar modules variously interconnected to each other. The failure of one of them can eventually affect the performance of the whole system. The growth in the capacities of large-scale PV plants entails ways to actively monitor and detect any incipient faults so that appropriate remedial actions can be taken before any major disruptions take place. In large-scale PV plants, monitoring systems are mostly built into the inverters to prevent anomalies on the utility side and to report the PV system status. However, it is extremely difficult to detect any malfunction in any string/module with only inverter level monitoring. In most of the cases, classical overcurrent protection devices that are connected at inverter level are unable to detect faults on the DC side due to reductions in the current levels seen by the overcurrent devices (Vargas et al., 2015). Even though the fault is detected at inverter level, it can be extremely difficult to identify and locate the fault within a
⁎
string. Among various problems in PV, particularly on the DC side of an array, a line-line fault is one such problem that can cause PV failure and lower system performance (Alam et al., 2015; Dhimish and Holmes, 2016; Garoudja et al., 2017). Commonly, line-line faults result from accidental short circuiting between two points in the array with different potentials, mainly due to water ingress, animal chewing, mechanical damage, or DC junction box corrosion (Zhao, 2011). Based on the fault existing in a single string or across two strings, line-line faults are categorised as intra-string or cross-string faults (Appiah et al., 2019; Flicker and Johnson, 2013; Han et al., 2018; Johnson and Flicker, 2013). However, this paper focuses on the intra-string line-line faults which are mostly undetected by conventional protection devices in large-scale PV systems when the fault occurs between the points with close electric potential i.e. low mismatch faults (Zhao et al., 2013). If undetected for a prolonged time these faults can propagate and damage the PV system components. Fault detection in a PV system is therefore crucial for maintaining the normal operation by providing early fault alarms through appropriate monitoring. Proper system monitoring is essential for reliable operation and power yield maximisation of solar PV systems (Dhoke et al., 2016). The levels of PV monitoring include inverter level, array level, combiner level and string level. PV string monitoring is best suited to understand the working status of each PV string in real time (Spataru et al., 2015; Zhao et al., 2014). Reduced energy yield over time in one string may be
Corresponding author. E-mail addresses: [email protected] (A. Dhoke), [email protected] (R. Sharma), [email protected] (T.K. Saha).
https://doi.org/10.1016/j.solener.2019.10.052 Received 2 November 2018; Received in revised form 23 September 2019; Accepted 22 October 2019 0038-092X/ © 2019 Published by Elsevier Ltd on behalf of International Solar Energy Society.
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Nomenclature PV DC ANN BNN TDR SSTDR ECM I-V MPPT OCPD GFPD BOS STC P Si Iapprox Iexact Li(actual)
Isc Ii G Ia n m ri I µ̂ Th
Photovoltaic Direct current Artificial neural network Bayesian neural network Time domain reflectometry Spread spectrum TDR Earth capacitance measurement Current-Voltage Maximum power point tracker Overcurrent protection device Ground fault protection device Balance od system Standard test condition Polycrystalline silicon Approximate current of the string in new array Actual current of the string in old array Actual fault location
Li Si T sold snew Ii(new) Lî mold mnew nratio nold Nnew
∧
Li (estimated)
Estimated fault location
Short circuit current String Current Solar irradiation Array current Number of parallel strings Number of series modules Residual Pre-set threshold Fault location String number Ambient temperature Slope of the line of old array Slope of the line of new array String current for new array Estimated fault location Number of modules in old string Number of modules in new string Ratio of old and new strings numbers Number of old strings Number of new strings
capacitance measurement (ECM) (Takashima et al., 2008; Takashima et al., 2009) are used to detect and localise a faulty PV module. Fault detection based on I-V characteristics compares the actual and expected electrical parameters from I to V characteristics of the array (Chine et al., 2014; Kang et al., 2012; Tina et al., 2016). The methods based on power loss analysis use the difference between reference and array power to identify faulty modules and strings (Chine et al., 2014; Chouder and Silvestre, 2010). All these methods are not able to identify the type of fault as well as the location. To identify the approximate location of a fault in an array, methods based on voltage and current measurement have shown reasonable accuracy (Chen and Wang, 2017; Davarifar et al., 2013; Silvestre et al., 2014). Further to this, methods based on module level monitoring are used in fault diagnosis. Guerriero et al. (2014) introduced a method which installs a wireless self-powered sensor on each PV module, and compares the measured and estimated maximum string power based on the MPPT algorithm proposed in (Guerriero et al., 2013). A module based multipurpose sensor, which is equipped with voltage, current, irradiance, temperature, and inertial monitoring, is used to detect faults in PV arrays by analysing its estimated and real efficiency (Ando et al., 2015; Firth et al., 2010). Although detecting faults by comparing estimated and measured parameters may be effective, these fault detection methods may suffer from various limitations such as an inability to promptly detect and locate faults, a lack of automated approaches for fault diagnosis without manual intervention, and a need for numerous sensors leading to costly schemes. Hu et al. (2015) introduced a method to detect PV faults based on optimised voltage sensors which used a two-section fault detection
diagnosed as a fault, but this requires a long offline processing time (at least a few hours or days) that may hinder the fault detection response and effectiveness (Vergura et al., 2009). For prompt fault detection, an alternative way is to monitor the instantaneous PV string current. Even with instantaneous PV string current monitoring without the weather information (i.e. solar irradiance and ambient temperature), it becomes difficult to identify whether the string is operating normally (under low irradiance conditions) or it is faulted by examining the current alone in a specific string. Therefore, it is necessary to compare each string’s performance and find which one is underperforming. The performance deviation as an outlier can be used to identify a faulty string. A number of fault detection methods for PV fault diagnosis are available in the literature. There are two main types of method: a process history based approach and a model based approach. The process history based method uses an empirical model derived from analysis of available data, which rely on computational and machine learning methods such as artificial neural network (ANN), bayesian neural network (BNN) and fuzzy logic. However, process history based methods require availability of a relevant dataset and training of the dataset that describes both healthy and faulty operating conditions in the PV system. On the other side, model based approaches compare analytically computed outputs with measured values and signal an alarm (Harrou et al., 2015). Several model-based approaches to fault detection in PV systems have been reported in the literature. Statistical and signal processing approaches are mainly based on the analysis of the waveform signals; for example, time domain reflectometry (TDR) (Takashima et al., 2006), spread spectrum TDR (SSTDR) and earth
Fig. 1. (a) A cabling scenario behind PV modules displaying group of cables bundled together, (b) DC junction box failure due to fault (Goss et al., 2011). 198
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future research plans.
scheme. This algorithm still requires a number of voltage sensors within PV strings, making it especially difficult for large-scale arrays. Many large-scale PV systems are susceptible to line-line faults due to improper cabling in which string cables are usually grouped together and passed through the backside of module mounting raceways as shown in Fig. 1(a). Also, lack of protection in a PV system may cause the junction box to fail leading to catastrophic fire as shown in Fig. 1(b). The novel contribution of this manuscript is to propose an approach to automatically detect and locate intra-string line-line faults in PV systems. Such an approach that is capable of automatically detecting and locating faults, though urgently needed for the management of large solar farms, is yet to be developed. The detection of faults is based on the generation of fault indicator signals called ‘residuals’ that are calculated by using the string current measurements. The proposed approach predicts the precise locations of the occurrence of line-line faults (e.g. string number and panel number) through the development of an analytical model that models fault location as a function of fault current levels, irradiance and PV module specifications. The paper also provides extensions of the proposed approach to arrays of any number of strings and any number of modules in each string to demonstrate how the same fault location prediction model can be used to predict fault locations for any array size. In particular, once a fault location prediction model is developed for a certain PV array topology, the paper proposes methods that allow use of the same fault location prediction model to any array size through straightforward scaling multipliers based on array sizes. The ability of the proposed approach is experimentally demonstrated using an experimental setup that is physically located with the premises of The University of Queensland’s Gatton Solar farm. The rest of this paper proceeds as follow: Section 2 briefly introduces the fundamentals of PV system configuration, background of line-line faults in PV array and its protection devices. Section 3 demonstrates the proposed fault detection and location approach. Section 4 presents simulation and experimental validation results for line-line faults. Section 5 provides extension of the approach to any array size followed by the discussion. Section 6 concludes the paper and provides
2. PV system description 2.1. Typical PV system configurations A typical PV system consists of a PV array, a maximum power point tracker (MPPT), an inverter and other balance of system (BOS) components (see Fig. 2). An array is comprised of series and parallel connected PV modules to obtain the desired voltage, current and power yield (Gokmen et al., 2012). In small-scale PV systems, strings are usually connected directly to the inverter. On the contrary, in largescale PV systems, strings are connected to the inverter through a combiner box. The PV array schematic shown in Fig. 2 consists of n strings connected in parallel with each string comprising m modules connected in series. The voltage generated by each string is m × V where V is the voltage across each individual module and, current generated at the array is n × I where I is the current through each string. Due to the structure of an array, each module in a string has the same current and each string in the array shares the same voltage. For the given array, total output power (Pout) can be evaluated as (m × V) × (n × I). This paper assumes the existence of string level current monitoring. The string current measurements are denoted as Ii , i ∈ [1, n], where subscript ‘i’ refers to the ith string measurement. The array current and voltage are given as Ia and Va, respectively. 2.2. PV array protection requirements OCPD and GFPD in Fig. 2 represent overcurrent protection devices and ground fault protection devices, respectively. OCPDs are connected to protect arrays from overcurrent faults and GFPDs are meant to protect against ground faults (Standards Australia, 2014). The purpose of connecting these protection devices is to protect cables and electrical equipment in the PV system. Particularly, OCPDs in large-scale PV plants are generally connected to inverters or combiner boxes where hundreds of strings are connected together. For example, at The
Fig. 2. Typical configuration of a PV system. 199
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University of Queensland’s (UQ) Gatton 3.26 MW Solar Farm, each inverter has an OCPD which is connected to 480 strings through a combiner box. With a large number of strings connected to a single OCPD, it is likely that intra-string line-line faults with relatively small mismatches may remain undetected due to the associated small fault current levels. Consequently, it can be extremely difficult to identify and locate the line-line fault in large-scale PV systems. PV systems also consist of bypass diodes and blocking diodes. According to the national electric code (NEC, 2014), bypass diodes are used for the PV module protection which prevents the reverse current flow from good module to faulty PV module. Blocking diodes are used differently than bypass diodes which prevent backfeed current from normal strings to faulty string. However, bypass diodes and blocking diodes are not a substitute for fault current protection. During the fault, the reverse current may be totally cut off by blocking diodes which may cause the OCPD to lose the ability to properly interrupt the fault, and often, they are the first point of failure in a PV system.
Table 1 Specifications of the PV module. Parameter
Value
Cells per module Module peak power (Pmax) Open circuit voltage (Voc) Short circuit current (Isc) Voltage at MPP Current at MPP Temperature coefficient of Voc Temperature coefficient of Isc
60 240 W 37.2 V 8.37 A 30.4 V 7.89 A −0.35%/°C 0.05%/°C
information required is the string current (Ii) and solar irradiation (G) which are generally available in PV plants. 2.4. Experimental PV system description To validate the findings of this paper, a 3.8 kW experimental system is designed. The experimental set-up is located at The University of Queensland’s 3.26 MW Solar Research Facility located in Gatton campus, Australia. The photograph of the experimental setup is shown in Fig. 3(a). The longitude and latitude of the site are 152°20′14.1″E and 27°33′41.5″S respectively. Sixteen polycrystalline silicon (p Si) solar photovoltaic modules (240 W each) manufactured by Trina Solar are used in the setup, with a tilt angle of 20°. The detailed specifications of the PV module at standard test conditions (STC) that are used in the experimental setup are given in Table 1. The sixteen modules are used to develop a PV array consisting of four strings with each string comprising of four modules connected in series. The first two strings are designed with MC4 connectors to create the faults physically. CR Magnetics (5210ADC) current sensors with 4–20 mA output and 1% accuracy are connected at the string end for current measurements. An Easylog portable data logger is used to store the measured parameters. Also, PV generated external power supply (consisting of a dedicated PV module, charge controller and a battery) is used for the measurement sensors. A 4 kW power resistor pack is connected as a load to the PV array. An adjustable OCPD (circuit breaker) of 15 A is connected to each string. The experimental setup connections with associated components are shown in Fig. 3(b).
2.3. Line-line faults in PV arrays Line-line faults are arbitrary and can occur between any two points in the PV array. Based on the fault in a single string or multi string, the line-line faults are categorised as intra-string (e.g. faults F1, F2 and F3 in Fig. 2) or cross-string (e.g. fault F4 in Fig. 2). The occurrence of a lineline fault results in topological changes within a PV array due to partial or total bypassing of modules within strings caused by line-line short circuits. The excursions of the currents and voltages associated with faulty strings relative to fault free strings can be used to detect fault occurrence and establish the location. However, the current of a faulty string mainly depends on the number of module mismatches (location mismatch), their module voltages and the fault impedance. The fault location is defined as the number of bypassed modules in terms of ‘location mismatch’. For example, faults F1, F2 and F3 marked in Fig. 2 correspond to location mismatches of 25%, 50% and 75% respectively (assuming 4 modules in a string). The objective of this paper is to detect and locate the intra-string line-line faults (e.g. F1, F2 and F3) in a PV array. The cross-string fault (F4) is not considered in this paper but the approach can be extended to detect that fault. The intra-string line-line faults can bring the current in the string down to a level that may not be sufficient to trip any protection device. Therefore, the systematic approach of the proposed method must first detect occurrence of the intra-string line-line fault. Second, identify the faulty string. Finally, locate the position within the string where the fault has occurred. In this approach, the only
3. Proposed fault detection and location This section summarises the proposed fault detection and location
Fig. 3. Experimental system used in fault analysis: (a) Picture, (b) Schematic. 200
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magnitude change can be seen only in string 1 while the rest of the string currents remain the same, indicating that an intra-string fault affects the current output of the corresponding string only. Each fault location represents bypassing one PV module which results in the loss of that module’s voltage. Since the faulted string is connected in parallel with other non-faulted strings, the voltage of modules in string 1 will increase to maintain same voltage across all the strings. It is also observed that the current magnitudes in string 1 represent negative values which are due to the back-feed current that flows from healthy strings to faulty strings to maintain the voltage of each string equal.
methodology. The overall approach of the proposed method is shown in Fig. 4. The proposed PV array fault diagnosis strategy is implemented in two stages. In stage 1 faults are detected followed by precise location of faults in stage 2. 3.1. Fault detection The objective of the fault detection is to identify an anomaly in the PV array’s operation. The approach taken to detect the occurrence of a fault is based on the generation of fault indicator signals called residuals. The residual generation is governed by the string current measurements and pre-determined threshold values. In order to formulate the proposed residual-based fault detection approach, let us denote the string current measurement of the ith string as Ii and the array current as Ia. Therefore, Ia can be written as:
3.2. Fault location If ri > εTh, i ∈ [1, n], then our next objective is to estimate the likely location of the occurrence of the fault within the ith string. It is expected that the location of a fault along with irradiance, the number of modules in a string, the number of strings in an array and temperatures collectively govern the level of fault current (Ii) through the faulty string. Consequently, it is safe to say that an expression to estimate fault location within a string can be expressed in the functional form as follows:
n
Ia =
∑ Ii. i=1
(1)
The proposed fault detection procedure is presented in the form of the following steps: Step 1: Calculate the mean of string current
I I¯a = a . n
∧
Li = f¯ (G, Ii , m , n, T ).
where Lî denote the estimated fault location within the i string. For a given PV array the values of m and n are fixed. Furthermore, the temperature (T) is assumed constant considering it has a weak effect on current, hence on the fault location. Therefore, for a given PV array the expression (6) can be reduced to the following functional form:
(2)
where I¯a is the mean of all string currents and n is the number of parallel strings. Step 2: Calculate the residual for each string For each ith string, residual (ri) can be calculated as
ri = |Ii − I¯a |, ∀ i = [1, n].
∧
Li = f (G, Ii ).
(3)
(7)
Our objective is to obtain a regression function f (G, Ii) such that the precise location of fault within a string can be estimated once the fault has occurred. For the derivation of a function f (G, Ii), the range of G is taken between 400 W/m2 and 1000 W/m2. The choice of this range is
Step 3: Detection of faults through residual evaluation In order to detect occurrence of a fault, we define a pre-set threshold value I µ̂ Th . I µ̂ Th governs the boundary around ri to ascertain the existence of an outlier within the string current measurements. Based on Eq. (3) and the choice of I µ̂ Th , fault detection is governed by the following law:
|ri | < εTh ⇒ No fault |ri | > εTh ⇒ Fault present in ith string.
(6) th
(4)
I µ̂ Th can be estimated using the following: εTh = sup ∥ri ∥2 , under no fault condition. ∀ i ∈ [1, n]
(5)
In Eq. (5), ‘sup’ represents the supremum and I µ̂ Th , calculated as per Eq. (5), signifies the calculation of the greatest lower bound on the norm bounded values of string current measurements under no fault conditions. In practice, the choice of I µ̂ Th is governed by practical considerations such as the number of modules and strings, solar irradiation level and operating temperature of each module. Some sample values of I µ̂ Th are discussed in the simulations and experimental results sections. In the event of simultaneous occurrence of intra-string faults with multiple residuals as per Eq. (4), it is likely that a cross-string lineline fault has occurred. While the focus of this paper is to illustrate and validate intra-string line-line fault detection and location, a similar approach can be developed for the detection and location of cross-string faults. The generalised block diagram of the fault detection approach is shown in Fig. 5(a). For each string, residual (ri) indicates a fault signal based on Eqs. (1)–(5). As an example, Fig. 5(b) represents the string current accompanying intra-string faults within string 1 at various module locations (Li), i ∈ [1,4 ] for a 4 × 4 array. For each fault location (Li), the respective current magnitudes (Ii) of string 1 are changing. An increase in the fault location results in decreased Ii, which improves the sensitivity of the residual signal. As shown in Fig. 5(b), current
Fig. 4. Flowchart of the proposed fault detection and location procedure. 201
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Fig. 5. (a) An outline of the proposed fault detection system based on residual generation, (b) An example of the current vs fault location characteristics for a 4 × 4 array with fault at different locations in string 1. The figure shows substantial current variations in string 1for different fault locations, while the currents in strings 2, 3 and 4 remains unaffected.
practically meaningful because in Queensland nearly 75% – 90% of the time on a clear sky day the irradiance levels are within 400–1000 W/ m2. To obtain the regression function for Eq. (7), a 3-dimensional data set between Li–G–Ii is obtained through a series of simulations performed using a simulation model developed in MATLAB/Simulink. Various array configurations are implemented in Simulink including 4 × 4, 6 × 3 and 15 × 4. Fig. 6 shows the data set obtained for the 15 × 4 configuration. The data set is obtained by running the simulation sweep for various fault location (between 1 and 15) and Solar irradiance (between 400 W/m2 and 1000 W/m2 in the steps of 100 W/ m2) combinations and the corresponding string (fault) current levels.
faults along the string. Fig. 7 also shows that the irradiance level shifts the current versus location characteristics up or down without changing the slope of the characteristics. The threshold ( I µ̂ Th ) is calculated using Eq. (5) and absolute values of threshold with respect to irradiation are given in Table 2. Table 2 demonstrates the ability of the proposed approach to detect and locate faults. As G and Li change the relative I1 also changes. It can be clearly observed that an increase in r1 is related to a decrease in I1 which allows prompt fault detection. Once the fault is detected, it is localised by following the procedure in Section 3.2. The estimated locations ∧
(
Li ) (estimated)
with respect to actual (Li(actual) ) shows a good trade-off be-
tween accuracies. It is noteworthy that to reduce the complexity and false alarms, this analysis used irradiances of 700 W/m2 and above. The fault diagnosis accuracy is expected to increase as the PV model is optimised further.
∧
In order to obtain Li as per Eq. (7), the dataset is fitted into a regression model using MATLAB’s polynomial surface fit function (Marthworks, 2017) to obtain a regression expression of the following form: ∧
Li = C1 + (C2 × G ) + (C3 × Ii ) + (C4 × G 2) + (C5 × G × Ii )+ (C6 × Ii 2) + (C7 × G 2 × Ii ) + (C8 × G × Ii 2) + (C9 × Ii3).
4.1.2. Case study: 6 × 3 PV array The fault location estimation expression in Eq. (8) is specifically fit for the 6 × 3 PV array configuration by following the procedure outlined in section 3.2. For validation, four different irradiation levels (between 700 W/m2 and 1000 W/m2 in the steps of 100 W/m2) are considered. The intra-string faults are created along string 1 for different irradiance conditions and the corresponding string currents are measured in simulations. Fig. 8 shows the results which display a nearly linear relationship between string 1 current and fault locations. The irradiance levels simply shift the characteristics up/down without altering the slope of the characteristics. The fault detection and location results are summarised in Table 3 which confirms the ability of the
(8)
Using the simulation generated dataset, we estimate the values for parameters (C1 to C9). A cubic fit is selected to estimate coefficients of a polynomial that fits a set of data in a least-squares sense. For the 15 × 4 array configuration corresponding to Fig. 6, a cubic fit was found to be the most suitable. 4. Simulation and experimental validation The proposed approach in Section 3 is validated in this section through simulations and experiments. 4.1. Simulation results In order to test the ability of the proposed approach to detect the faults and estimate the likelihood location, a series of line-line faults within the string are used in simulation. In this section, the proposed method is applied on three PV array topologies to demonstrate its validity on different PV array configurations: 4 × 4, 6 × 3 and 15 × 4 PV arrays. 4.1.1. Case Study: 4 × 4 PV array In this case, there are four strings and each string has four series connected PV modules. The intra-string line-line faults are created under different irradiance levels as seen in Fig. 7. The faults are created at different locations along string 1. For each fault scenario, the residuals for each string are calculated and the fault location estimated using Eqs. (3) and (6), respectively. Fig. 7 shows that the fault current level through the faulty string changes linearly with the location of the
Fig. 6. Plot showing the dependence of string current on fault location and irradiance levels for 15 × 4 array. 202
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with four strings where each string has 15 series connected PV modules is simulated for analysis. Typically, 400 V bus voltage is required for a 220 V 50 Hz single-phase grid, and a 600 V bus voltage is required for a three-phase grid (Standards Australia, 2014). In this case, 15 PV modules per string are considered to maintain the array voltage within the given range. Intra-string line-line faults with different locations are created in the PV array under various irradiance levels. The string (fault) current magnitudes with estimated fault locations are represented in Fig. 9(a). With increasing number of modules, the linearity between the string current and fault location begins to somewhat diminish especially for low irradiance levels of around 700 W/m2. This trend is reflected in the accuracy of the fault location estimation (summarised in Fig. 9(b)). The linearity at the closest fault location is slightly lost due to negligible change in Ii. This change is obvious, as the string is comprised of more modules (i.e. 15 × 4), their respective change in Ii towards a fault location closer to string end is nearly constant. Considering that the irradiance levels on a near clear sky reaches above 700 W/m2 for most of the time (e.g. 9 am to 4 pm), the loss of linearity causing lower fault location accuracy is not expected to compromise the fault location performance in practical systems.
Fig. 7. String current for various fault locations and irradiation in 4 × 4 array. Table 2 Simulation validation of fault detection and location approach on 4 × 4 array. G
String Current(I1)
Detection Residual(r1)
1000 900 800 700
2.98 −2.62 −9.43 −17.62
0.60 2.62 11.50 21.44
Location
εTh
0.55 0.50 0.45 0.40
Li(actual)
1 2 3 4
Accuracy ∧ Li (estimated)
1.0 2.03 3.03 4.0
4.2. Experimental results 100% 99% 99% 100%
The approach for fault detection and location proposed in section 3 is experimentally validated on a setup shown in Fig. 3(a). Two experimental PV array configurations, 4 × 4 and 6 × 3, are developed to demonstrate the applicability of the proposed approach for different array topologies.
4.2.1. Validation on a 4 × 4 array The PV array is arranged with four strings with each string comprised of four modules connected in series. Current sensors are placed at each string’s end to analyse the impact of line-line faults on string current magnitudes. For experimentation, random intra-string faults are created by short circuits between M14 till M11 in string 1 (See Fig. 3(b)). The faults were created when the G levels at the experimental setup location were (i) 700 W/m2 and (ii) 950 W/m2. For each irradiance level, the faults are created along string 1 and the corresponding string currents are measured. The results obtained from these experiments are summarised in Table 4. The threshold is calculated using Eq. (5) under a no fault condition at a corresponding irradiance. The results in Table 4 confirm the ability of the proposed approach to accurately detect and locate intra-string line-line faults. The performance in estimating the fault location is particularly noteworthy. The proposed approach is capable of estimating the location with high level of accuracy at different irradiance levels.
Fig. 8. String currents for various fault locations and irradiation in 6 × 3 array. Table 3 Simulation validation of fault detection and location on 6 × 3 array. G
String Current(I1)
Detection Residual (r1)
εTh
Location Li(actual)
Accuracy ∧
4.2.2. Validation on a 6 × 3 array The experimental validation of the proposed approach is repeated on a 6 × 3 PV array. For evaluation, intra-string faults are introduced at various locations within string 1 sequentially from location 1 to location 6. The fault detection and location estimation results are given in Table 5. The threshold ( I µ̂ Th ), calculated using Eq. (5), is found to be 0.45 and 0.5 at 750 W/m2 and 800 W/m2 irradiance levels, respectively during fault free conditions. The results confirm that the proposed approach is capable of successfully detecting and locating faults regardless of the array configuration. The expected fault location can provide the exact fault location
Li (estimated)
1000 900 800 700 600 500
5.60 3.31 0.84 −1.91 −5.47 −8.39
0.67 1.84 3.76 6.27 9.24 13.05
0.65 0.60 0.55 0.50 0.45 0.40
1 2 3 4 5 6
0.98 1.99 3.02 4.08 5.07 6.10
98% 99% 99% 98% 98% 98%
approach to accurately detect faults and locate them in terms of the faulty module number.
∧
after rounding of the
Li values. (estimated)
Also, it is noticeable from Table 5
that the accuracy of the estimated fault location increases with an increase in experimental fault location.
4.1.3. Case Study: 15 × 4 PV array In order to make the proposed method more realistic, a PV array 203
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Fig. 9. (a) Location vs fault current characteristics for 15 × 4 array, (b) The corresponding accuracy of the proposed fault location approach.
5.1. Variation in number of modules
Table 4 Experimental results of estimated and actual fault locations with different irradiance in 4 × 4 array. G
700 700 700 700 950 950 950 950
String Current (I1)
5.10 1.70 −4.40 −12.7 1.50 −5.80 −14.1 −24.5
Detection
Location
To generalise the proposed approach as per Eq. (6), the effect of a varying number of modules (m) in a string is considered. The rest of the parameters (G, n, T) are kept unchanged. The relationship between the fault location and string current levels for different string sizes is analysed. Different arrays with a varying number of modules within each string (3 × 3, 5 × 3, 7 × 3, 9 × 3) are simulated. The string current magnitudes vs fault locations are gathered and presented in Fig. 10. From Fig. 10, it is observed that even though the number of modules in a string changes, the range of current magnitudes remains the same. With this observation, the following expression is proposed:
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.325 3.575 9.5 17.35 5.5 12.8 21.1 31.5
0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4
1 2 3 4 1 2 3 4
1.18 1.80 2.69 3.58 1.12 2.09 3.08 4.05
85% 90% 90% 90% 89% 95% 97% 99%
snew = sold ×
750 750 750 750 750 750 800 800 800 800 800 800
String Current (I1)
6.5 5.2 2.6 −2.7 −4.9 −6.5 6.7 5.2 2.1 −2.92 −5.47 −6.7
Detection
Location
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.4 0.9 3.5 8.8 11.0 12.6 0.67 1.13 4.23 9.25 11.8 12.73
0.45 0.45 0.45 0.45 0.45 0.45 0.5 0.5 0.5 0.5 0.5 0.5
1 2 3 4 5 6 1 2 3 4 5 6
1.16 2.49 2.42 4.64 5.40 6.16 1.14 2.40 3.37 4.41 5.35 6.14
(9)
where sold denotes the slope of the line which characterises current versus location characteristics, snew represents the slope corresponding to the current versus location characteristics for the new array configuration for which the fault location expression is to be remodelled, mold and mnew represent number of modules in the existing string and new string, respectively. For validation, let us consider a scenario whereby we want to obtain the fault location expression for a 6 × 3 array using an expression derived and validated for a 4 × 3 array. In this situation, we can obtain the slope of the line characterising current vs fault location using Eq. (9). Fig. 11 shows the comparison between the actual and approximated characteristics. Once snew is known, the new current (Ii(new)) for any string size can be obtained using Eq. (10).
Table 5 Experimental results of estimated and actual fault locations with different irradiance in 4 × 4 array. G
mold . mnew
86% 80% 81% 86% 93% 97% 88% 83% 89% 91% 93% 98%
Ii (new) = Ii ×
mold . mnew
(10)
where mold and mnew are number of modules in old and new array respectively. The fault location expression for a new array size can be obtained by substituting Eq. (10) in Eq. (7). The approach is validated on 6 × 3 arrays and results are revealed in Table 6. The results confirm the ability of the proposed approach to generalise for m number of modules in a string with reasonable fault location accuracy.
5. Extension of approach for any array size In section 3, the proposed approach for fault location is developed for fixed number of modules and strings. Ideally, it is desirable to extend the approach for any array size so as to enhance its portability to various PV array configurations with minimal effort. Given a fault location estimation expression for a specific array configuration, this section proposes approaches to re-model the expression to suit other PV array configurations with varying modules, strings and temperature.
5.2. Variation in number of strings The fault location approach discussed in Section 3 is considered for a varying number of strings (n) in an array using Eq. (6). The parameters (G, m, T) are kept constant to analyse fault current magnitudes with a change in string numbers. To get insight into string current (Ii) and fault location (Li), various array combinations with number of strings (4 × 3, 4 × 4, 4 × 5, 4 × 6) are simulated and plotted as shown in Fig. 12. The string current magnitudes (Ii) in Fig. 12 show a linear shift with number of parallel strings. Therefore, scaling of the string 204
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Fig. 10. Fault current magnitudes with varying number of modules in a string (a) 3 × 3, (b) 5 × 3, (c) 7 × 3, (d) 9 × 3 and locations at 1000 W/m2.
Fig. 12. Fault current magnitudes with varying string numbers at 1000 W/m2. Fig. 11. Verification of approximate and exact current characteristics for 6 × 3 array.
snew = nratio × nold × nnew .
where snew is the slope of the line which characterises current versus location of new string, nratio denotes ratio of old and new strings numbers, nold and nnew represents number of old and new strings respectively. To validate the effect of string characteristics, we want to obtain the expression for a 4 × 6 array using an expression derived and validated for a 4 × 4 array. In this case, the slope of the line characterising current vs fault location can be obtained using Eq. (11). Fig. 13 shows a trend in the string fault currents with variations in number of strings. This trend is captured in the form of actual and approximated characteristics. Once the slope of snew is known, the new string current for any string numbers in array can be obtained through Eq. (12). The expression for estimated fault location is given in Eq. (12):
Table 6 Fault location estimation in a 6 × 3 array using an expression derived from a 4 × 3 array. Irradiation(G)
1000 1000 1000 1000 1000 1000
String current (Ii)
5.60 2.15 −1.84 −6.34 −11.31 −16.77
Location
Accuracy
Li(actual)
∧ Li (estimated)
1 2 3 4 5 6
0.63 1.52 3.54 4.48 5.33 6.29
(11)
63% 76% 84% 89% 93% 95%
Ii (new) = Ii × numbers with the string current magnitudes can be used for approximating the characteristics of any number of strings. From the characteristics observed in Fig. 12, the following expression is proposed:
nnew . nold
(12)
The estimated fault location expression for the new array size can be obtained by substituting Eq. (12) in Eq. (7). The given approach is verified on 4 × 4 array and the results are shown in Table 7. The results confirm that the proposed approach is capable of generalising fault 205
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with MPPT. The results are summarised in Table 8. From the results, it can be seen that the net effect of MPPT is the overall slight drop in currents in all the strings when compared with without MPPT. This drop is caused by the voltage adjustments by MPPT to maximise power yield. Nonetheless, the relative current differential between the faulted and fault-free strings is maintained and the pattern in which fault current varies relative to fault location is the same without and with MPPT. The results confirm that the pattern of current variations relative to fault location is consistent between without and with MPPT. Consequently, the proposed method is valid for both without and with MPPT scenarios.
5.5. Discussion It is observed from the results that the change in string current has strong correlation with fault detection and location. During an intrasting fault, the short circuited path bypasses modules in the faulted string. As a result, the bypassed module does not contribute any voltage into the string. Since strings are connected in parallel, the array maintains the same voltage across all strings by reducing the current of the faulted string. As a result, the faulty string’s current is different to a normal string’s and is governed by the mismatch created through bypassed modules. At the occurrence of a fault with low mismatch (low fault location), the faulty string’s current magnitude is nearly the same as a normal string’s which results in diminishing detection accuracy. Additionally, irradiation levels during a fault influences the string current magnitude. Due to that, the residual of the faulty string in low irradiation appears close to the threshold boundary and makes fault detection cumbersome. Similarly, fault location estimation accuracy is slightly lower during low irradiation. To overcome these issues, selection of irradiation levels between 400 W/m2 and 1000 W/m2 is found sufficient for fault detection and location. However, the proposed method can still work in low irradiation conditions with a lower accuracy. The same results are observed in the other research (Mallor et al., 2017; Platon et al., 2015; Yi and Etemadi, 2017) which underperforms in low irradiation conditions. Moreover, the accuracy of the estimated fault location is exceptional when the proposed method is developed for the same array size of the system under test. However, it limits the approach being used for any size of array. To overcome this issue, the approach is extended for any array size with compromises in fault location accuracy. It is also observed that the effect of small mismatch (location) faults on string current levels is nearly the same with a large array size. For such cases, the performance of the proposed method is expected to improve if accurate measurements are taken. Besides that, most of the PV arrays are usually protected by OCPD which provides isolation if the fault current (backfeed current) reaches protection settings. However, from the results it is observed that the string current can reach to the OCPD fault current setting only when the fault location mismatch is above a 75%. Even if the OCPD detect the fault, it may not locate the fault within the string. Therefore, the proposed method has good potential in fault diagnosis in addition to the use of OCPDs. The proposed method only uses string current and irradiance, which
Fig. 13. Verification of approximate and exact current characteristics for 4 × 6 array. Table 7 Fault location estimation for 4 × 4 array using an expression derived from a 4 × 6 array. Irradiation (G)
1000 1000 1000 1000
String current (Ii)
Location
2.96 −4.64 −13.98 −25.16
Accuracy
Li(actual)
∧ Li (estimated)
1 2 3 4
1.43 2.38 3.32 4.28
69% 84% 90% 93%
location estimation for n number of strings with good accuracy. 5.3. Variation in temperature Since the temperature (T) has less dependency on the Isc of a PV module, the string current may be affected only slightly with major change in T and is therefore usually ignored. The proposed approach is verified by changing the T as per Eq. (6) and keeping other parameters (G, m, n) constant. It is observed from the simulations that the temperature change has a negligible effect on the string currents which does not affect the fault detection and location. 5.4. Effect of MPPT on proposed method In order to evaluate the effect of MPPT on the validity of the proposed method, additional simulations are performed with MPPT. In particular, in order to clarify the effects of MPPT on the proposed method, simulation analysis is performed with MPPT on the 4 × 4 array for a range of irradiance conditions. Faults are created in string 1 only, therefore, the residual values (r1) for string 1 only are shown. Different irradiance levels and fault locations are considered to encompass the effects of irradiance levels and fault locations relative to without and Table 8 Experimental results of the proposed method with MPPT effect on 4 × 4 array. G (W/m2)
Detection
Location
String current
1000 900 800 700
I1
I2
I3
I4
2.87 −2.39 −8.21 −15.56
7.62 6.98 6.14 4.89
7.62 6.98 6.14 4.89
7.62 6.98 6.14 4.89
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.55 0.50 0.45 0.40
0.31 0.33 0.34 0.36
1 2 3 4
0.97 2.14 3.36 4.18
206
97% 93% 89% 96%
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References
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6. Conclusions The proposed fault detection and location algorithm for a PV system is based on the residual generation and regression coefficient extraction. The results successfully demonstrate the ability of the proposed method to successfully detect and locate intra-string line-line faults that otherwise may remain undetected in large-scale solar farms. The method uses residual based outlier detection governed by the pre-defined threshold. Any outlier outside the boundary of the threshold indicates a fault. Once the fault is detected, the fault location algorithm estimates the fault location within the string. The fault location algorithm uses regression based expressions which requires only irradiance and string current magnitudes. Both theoretical simulations and experimental validations are carried out, which have shown promising performance of the proposed method. Further, the fault location approach has been generalised and validated for any size of array. Through the analysis it is found that string level monitoring can significantly enhance the visibility on the dc side of the PV plants regardless of the plant size. Noting that the existing protection devices are primarily meant to protect inverters and dc side fires, the proposed approach complements the existing protection equipment by prompt detection and location of PV faults, eventually benefitting the longevity of PV modules. A natural extension of the proposed approach is to make the proposed approach adaptive to variations in module degradation condition. This work will be undertaken in the near future.
Acknowledgements The proposed fault detection and location approach was submitted to IP Australia on 31st October 2018 as an Australian Provisional Patent (No. 2018904145). This work was performed in part or in full using equipment and infrastructure funded by the Australian Federal Government’s Department of Education AGL Solar PV Education Investment Fund Research Infrastructure Project. The University of Queensland is the Lead Research Organization in partnership with AGL, First Solar and the University of New South Wales. The authors gratefully acknowledge the Ministry of Social Justice and Welfare, Maharashtra State, Government of India for a Doctoral Fellowship (Amit Dhoke, No. DSW/Edu/2014-2015/D-IV/114). 207
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Solar Energy journal homepage: www.elsevier.com/locate/solener
An approach for fault detection and location in solar PV systems ⁎
T
Amit Dhoke, Rahul Sharma , Tapan Kumar Saha School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Fault detection and location Fault current PV protection PV system String monitoring
The task of fault detection and diagnosis in large-scale photovoltaic (PV) plants is expected to be a major challenge as more and more plants with increasingly large capacities continue to come into existence. To maintain safety, reliability, and productivity of large-scale PV plants it is essential to develop approaches that allow automatic detection and location of any mal-operation among thousands of PV modules. This paper proposes an approach to detect PV plant faults through the generation of fault indicator signals called ‘residuals’ for each string and the comparison of residuals with a threshold value. Furthermore, a regression-based approach is proposed to estimate fault location as a function of fault current and irradiance level measurements. The proposed approach is demonstrated by specifically focusing on intra-string line-line faults. Various line-line fault case studies are verified through simulations and validated on an experimental setup in a solar PV plant. Initially the approach is developed for a fixed size PV array, but subsequently extensions of the proposed approach to any array size are developed and presented. The generalisation potential of the approach is demonstrated through successful validation on multiple PV array configurations.
1. Introduction Solar photovoltaic (PV) growth is greatly increasing worldwide due to its technical, economical and environmental benefits. To meet the energy demand, currently, PV is the most promising source of sustainable power generation. With the growing energy demand, largescale PV installations are gaining more attention to meet the energy targets. Thousands of large-scale PV plants are in the commissioning phase to meet hundreds of giga-Watts of demand. A large-scale PV system comprises of a very large number (hundreds of thousands) of solar modules variously interconnected to each other. The failure of one of them can eventually affect the performance of the whole system. The growth in the capacities of large-scale PV plants entails ways to actively monitor and detect any incipient faults so that appropriate remedial actions can be taken before any major disruptions take place. In large-scale PV plants, monitoring systems are mostly built into the inverters to prevent anomalies on the utility side and to report the PV system status. However, it is extremely difficult to detect any malfunction in any string/module with only inverter level monitoring. In most of the cases, classical overcurrent protection devices that are connected at inverter level are unable to detect faults on the DC side due to reductions in the current levels seen by the overcurrent devices (Vargas et al., 2015). Even though the fault is detected at inverter level, it can be extremely difficult to identify and locate the fault within a
⁎
string. Among various problems in PV, particularly on the DC side of an array, a line-line fault is one such problem that can cause PV failure and lower system performance (Alam et al., 2015; Dhimish and Holmes, 2016; Garoudja et al., 2017). Commonly, line-line faults result from accidental short circuiting between two points in the array with different potentials, mainly due to water ingress, animal chewing, mechanical damage, or DC junction box corrosion (Zhao, 2011). Based on the fault existing in a single string or across two strings, line-line faults are categorised as intra-string or cross-string faults (Appiah et al., 2019; Flicker and Johnson, 2013; Han et al., 2018; Johnson and Flicker, 2013). However, this paper focuses on the intra-string line-line faults which are mostly undetected by conventional protection devices in large-scale PV systems when the fault occurs between the points with close electric potential i.e. low mismatch faults (Zhao et al., 2013). If undetected for a prolonged time these faults can propagate and damage the PV system components. Fault detection in a PV system is therefore crucial for maintaining the normal operation by providing early fault alarms through appropriate monitoring. Proper system monitoring is essential for reliable operation and power yield maximisation of solar PV systems (Dhoke et al., 2016). The levels of PV monitoring include inverter level, array level, combiner level and string level. PV string monitoring is best suited to understand the working status of each PV string in real time (Spataru et al., 2015; Zhao et al., 2014). Reduced energy yield over time in one string may be
Corresponding author. E-mail addresses: [email protected] (A. Dhoke), [email protected] (R. Sharma), [email protected] (T.K. Saha).
https://doi.org/10.1016/j.solener.2019.10.052 Received 2 November 2018; Received in revised form 23 September 2019; Accepted 22 October 2019 0038-092X/ © 2019 Published by Elsevier Ltd on behalf of International Solar Energy Society.
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Nomenclature PV DC ANN BNN TDR SSTDR ECM I-V MPPT OCPD GFPD BOS STC P Si Iapprox Iexact Li(actual)
Isc Ii G Ia n m ri I µ̂ Th
Photovoltaic Direct current Artificial neural network Bayesian neural network Time domain reflectometry Spread spectrum TDR Earth capacitance measurement Current-Voltage Maximum power point tracker Overcurrent protection device Ground fault protection device Balance od system Standard test condition Polycrystalline silicon Approximate current of the string in new array Actual current of the string in old array Actual fault location
Li Si T sold snew Ii(new) Lî mold mnew nratio nold Nnew
∧
Li (estimated)
Estimated fault location
Short circuit current String Current Solar irradiation Array current Number of parallel strings Number of series modules Residual Pre-set threshold Fault location String number Ambient temperature Slope of the line of old array Slope of the line of new array String current for new array Estimated fault location Number of modules in old string Number of modules in new string Ratio of old and new strings numbers Number of old strings Number of new strings
capacitance measurement (ECM) (Takashima et al., 2008; Takashima et al., 2009) are used to detect and localise a faulty PV module. Fault detection based on I-V characteristics compares the actual and expected electrical parameters from I to V characteristics of the array (Chine et al., 2014; Kang et al., 2012; Tina et al., 2016). The methods based on power loss analysis use the difference between reference and array power to identify faulty modules and strings (Chine et al., 2014; Chouder and Silvestre, 2010). All these methods are not able to identify the type of fault as well as the location. To identify the approximate location of a fault in an array, methods based on voltage and current measurement have shown reasonable accuracy (Chen and Wang, 2017; Davarifar et al., 2013; Silvestre et al., 2014). Further to this, methods based on module level monitoring are used in fault diagnosis. Guerriero et al. (2014) introduced a method which installs a wireless self-powered sensor on each PV module, and compares the measured and estimated maximum string power based on the MPPT algorithm proposed in (Guerriero et al., 2013). A module based multipurpose sensor, which is equipped with voltage, current, irradiance, temperature, and inertial monitoring, is used to detect faults in PV arrays by analysing its estimated and real efficiency (Ando et al., 2015; Firth et al., 2010). Although detecting faults by comparing estimated and measured parameters may be effective, these fault detection methods may suffer from various limitations such as an inability to promptly detect and locate faults, a lack of automated approaches for fault diagnosis without manual intervention, and a need for numerous sensors leading to costly schemes. Hu et al. (2015) introduced a method to detect PV faults based on optimised voltage sensors which used a two-section fault detection
diagnosed as a fault, but this requires a long offline processing time (at least a few hours or days) that may hinder the fault detection response and effectiveness (Vergura et al., 2009). For prompt fault detection, an alternative way is to monitor the instantaneous PV string current. Even with instantaneous PV string current monitoring without the weather information (i.e. solar irradiance and ambient temperature), it becomes difficult to identify whether the string is operating normally (under low irradiance conditions) or it is faulted by examining the current alone in a specific string. Therefore, it is necessary to compare each string’s performance and find which one is underperforming. The performance deviation as an outlier can be used to identify a faulty string. A number of fault detection methods for PV fault diagnosis are available in the literature. There are two main types of method: a process history based approach and a model based approach. The process history based method uses an empirical model derived from analysis of available data, which rely on computational and machine learning methods such as artificial neural network (ANN), bayesian neural network (BNN) and fuzzy logic. However, process history based methods require availability of a relevant dataset and training of the dataset that describes both healthy and faulty operating conditions in the PV system. On the other side, model based approaches compare analytically computed outputs with measured values and signal an alarm (Harrou et al., 2015). Several model-based approaches to fault detection in PV systems have been reported in the literature. Statistical and signal processing approaches are mainly based on the analysis of the waveform signals; for example, time domain reflectometry (TDR) (Takashima et al., 2006), spread spectrum TDR (SSTDR) and earth
Fig. 1. (a) A cabling scenario behind PV modules displaying group of cables bundled together, (b) DC junction box failure due to fault (Goss et al., 2011). 198
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future research plans.
scheme. This algorithm still requires a number of voltage sensors within PV strings, making it especially difficult for large-scale arrays. Many large-scale PV systems are susceptible to line-line faults due to improper cabling in which string cables are usually grouped together and passed through the backside of module mounting raceways as shown in Fig. 1(a). Also, lack of protection in a PV system may cause the junction box to fail leading to catastrophic fire as shown in Fig. 1(b). The novel contribution of this manuscript is to propose an approach to automatically detect and locate intra-string line-line faults in PV systems. Such an approach that is capable of automatically detecting and locating faults, though urgently needed for the management of large solar farms, is yet to be developed. The detection of faults is based on the generation of fault indicator signals called ‘residuals’ that are calculated by using the string current measurements. The proposed approach predicts the precise locations of the occurrence of line-line faults (e.g. string number and panel number) through the development of an analytical model that models fault location as a function of fault current levels, irradiance and PV module specifications. The paper also provides extensions of the proposed approach to arrays of any number of strings and any number of modules in each string to demonstrate how the same fault location prediction model can be used to predict fault locations for any array size. In particular, once a fault location prediction model is developed for a certain PV array topology, the paper proposes methods that allow use of the same fault location prediction model to any array size through straightforward scaling multipliers based on array sizes. The ability of the proposed approach is experimentally demonstrated using an experimental setup that is physically located with the premises of The University of Queensland’s Gatton Solar farm. The rest of this paper proceeds as follow: Section 2 briefly introduces the fundamentals of PV system configuration, background of line-line faults in PV array and its protection devices. Section 3 demonstrates the proposed fault detection and location approach. Section 4 presents simulation and experimental validation results for line-line faults. Section 5 provides extension of the approach to any array size followed by the discussion. Section 6 concludes the paper and provides
2. PV system description 2.1. Typical PV system configurations A typical PV system consists of a PV array, a maximum power point tracker (MPPT), an inverter and other balance of system (BOS) components (see Fig. 2). An array is comprised of series and parallel connected PV modules to obtain the desired voltage, current and power yield (Gokmen et al., 2012). In small-scale PV systems, strings are usually connected directly to the inverter. On the contrary, in largescale PV systems, strings are connected to the inverter through a combiner box. The PV array schematic shown in Fig. 2 consists of n strings connected in parallel with each string comprising m modules connected in series. The voltage generated by each string is m × V where V is the voltage across each individual module and, current generated at the array is n × I where I is the current through each string. Due to the structure of an array, each module in a string has the same current and each string in the array shares the same voltage. For the given array, total output power (Pout) can be evaluated as (m × V) × (n × I). This paper assumes the existence of string level current monitoring. The string current measurements are denoted as Ii , i ∈ [1, n], where subscript ‘i’ refers to the ith string measurement. The array current and voltage are given as Ia and Va, respectively. 2.2. PV array protection requirements OCPD and GFPD in Fig. 2 represent overcurrent protection devices and ground fault protection devices, respectively. OCPDs are connected to protect arrays from overcurrent faults and GFPDs are meant to protect against ground faults (Standards Australia, 2014). The purpose of connecting these protection devices is to protect cables and electrical equipment in the PV system. Particularly, OCPDs in large-scale PV plants are generally connected to inverters or combiner boxes where hundreds of strings are connected together. For example, at The
Fig. 2. Typical configuration of a PV system. 199
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University of Queensland’s (UQ) Gatton 3.26 MW Solar Farm, each inverter has an OCPD which is connected to 480 strings through a combiner box. With a large number of strings connected to a single OCPD, it is likely that intra-string line-line faults with relatively small mismatches may remain undetected due to the associated small fault current levels. Consequently, it can be extremely difficult to identify and locate the line-line fault in large-scale PV systems. PV systems also consist of bypass diodes and blocking diodes. According to the national electric code (NEC, 2014), bypass diodes are used for the PV module protection which prevents the reverse current flow from good module to faulty PV module. Blocking diodes are used differently than bypass diodes which prevent backfeed current from normal strings to faulty string. However, bypass diodes and blocking diodes are not a substitute for fault current protection. During the fault, the reverse current may be totally cut off by blocking diodes which may cause the OCPD to lose the ability to properly interrupt the fault, and often, they are the first point of failure in a PV system.
Table 1 Specifications of the PV module. Parameter
Value
Cells per module Module peak power (Pmax) Open circuit voltage (Voc) Short circuit current (Isc) Voltage at MPP Current at MPP Temperature coefficient of Voc Temperature coefficient of Isc
60 240 W 37.2 V 8.37 A 30.4 V 7.89 A −0.35%/°C 0.05%/°C
information required is the string current (Ii) and solar irradiation (G) which are generally available in PV plants. 2.4. Experimental PV system description To validate the findings of this paper, a 3.8 kW experimental system is designed. The experimental set-up is located at The University of Queensland’s 3.26 MW Solar Research Facility located in Gatton campus, Australia. The photograph of the experimental setup is shown in Fig. 3(a). The longitude and latitude of the site are 152°20′14.1″E and 27°33′41.5″S respectively. Sixteen polycrystalline silicon (p Si) solar photovoltaic modules (240 W each) manufactured by Trina Solar are used in the setup, with a tilt angle of 20°. The detailed specifications of the PV module at standard test conditions (STC) that are used in the experimental setup are given in Table 1. The sixteen modules are used to develop a PV array consisting of four strings with each string comprising of four modules connected in series. The first two strings are designed with MC4 connectors to create the faults physically. CR Magnetics (5210ADC) current sensors with 4–20 mA output and 1% accuracy are connected at the string end for current measurements. An Easylog portable data logger is used to store the measured parameters. Also, PV generated external power supply (consisting of a dedicated PV module, charge controller and a battery) is used for the measurement sensors. A 4 kW power resistor pack is connected as a load to the PV array. An adjustable OCPD (circuit breaker) of 15 A is connected to each string. The experimental setup connections with associated components are shown in Fig. 3(b).
2.3. Line-line faults in PV arrays Line-line faults are arbitrary and can occur between any two points in the PV array. Based on the fault in a single string or multi string, the line-line faults are categorised as intra-string (e.g. faults F1, F2 and F3 in Fig. 2) or cross-string (e.g. fault F4 in Fig. 2). The occurrence of a lineline fault results in topological changes within a PV array due to partial or total bypassing of modules within strings caused by line-line short circuits. The excursions of the currents and voltages associated with faulty strings relative to fault free strings can be used to detect fault occurrence and establish the location. However, the current of a faulty string mainly depends on the number of module mismatches (location mismatch), their module voltages and the fault impedance. The fault location is defined as the number of bypassed modules in terms of ‘location mismatch’. For example, faults F1, F2 and F3 marked in Fig. 2 correspond to location mismatches of 25%, 50% and 75% respectively (assuming 4 modules in a string). The objective of this paper is to detect and locate the intra-string line-line faults (e.g. F1, F2 and F3) in a PV array. The cross-string fault (F4) is not considered in this paper but the approach can be extended to detect that fault. The intra-string line-line faults can bring the current in the string down to a level that may not be sufficient to trip any protection device. Therefore, the systematic approach of the proposed method must first detect occurrence of the intra-string line-line fault. Second, identify the faulty string. Finally, locate the position within the string where the fault has occurred. In this approach, the only
3. Proposed fault detection and location This section summarises the proposed fault detection and location
Fig. 3. Experimental system used in fault analysis: (a) Picture, (b) Schematic. 200
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magnitude change can be seen only in string 1 while the rest of the string currents remain the same, indicating that an intra-string fault affects the current output of the corresponding string only. Each fault location represents bypassing one PV module which results in the loss of that module’s voltage. Since the faulted string is connected in parallel with other non-faulted strings, the voltage of modules in string 1 will increase to maintain same voltage across all the strings. It is also observed that the current magnitudes in string 1 represent negative values which are due to the back-feed current that flows from healthy strings to faulty strings to maintain the voltage of each string equal.
methodology. The overall approach of the proposed method is shown in Fig. 4. The proposed PV array fault diagnosis strategy is implemented in two stages. In stage 1 faults are detected followed by precise location of faults in stage 2. 3.1. Fault detection The objective of the fault detection is to identify an anomaly in the PV array’s operation. The approach taken to detect the occurrence of a fault is based on the generation of fault indicator signals called residuals. The residual generation is governed by the string current measurements and pre-determined threshold values. In order to formulate the proposed residual-based fault detection approach, let us denote the string current measurement of the ith string as Ii and the array current as Ia. Therefore, Ia can be written as:
3.2. Fault location If ri > εTh, i ∈ [1, n], then our next objective is to estimate the likely location of the occurrence of the fault within the ith string. It is expected that the location of a fault along with irradiance, the number of modules in a string, the number of strings in an array and temperatures collectively govern the level of fault current (Ii) through the faulty string. Consequently, it is safe to say that an expression to estimate fault location within a string can be expressed in the functional form as follows:
n
Ia =
∑ Ii. i=1
(1)
The proposed fault detection procedure is presented in the form of the following steps: Step 1: Calculate the mean of string current
I I¯a = a . n
∧
Li = f¯ (G, Ii , m , n, T ).
where Lî denote the estimated fault location within the i string. For a given PV array the values of m and n are fixed. Furthermore, the temperature (T) is assumed constant considering it has a weak effect on current, hence on the fault location. Therefore, for a given PV array the expression (6) can be reduced to the following functional form:
(2)
where I¯a is the mean of all string currents and n is the number of parallel strings. Step 2: Calculate the residual for each string For each ith string, residual (ri) can be calculated as
ri = |Ii − I¯a |, ∀ i = [1, n].
∧
Li = f (G, Ii ).
(3)
(7)
Our objective is to obtain a regression function f (G, Ii) such that the precise location of fault within a string can be estimated once the fault has occurred. For the derivation of a function f (G, Ii), the range of G is taken between 400 W/m2 and 1000 W/m2. The choice of this range is
Step 3: Detection of faults through residual evaluation In order to detect occurrence of a fault, we define a pre-set threshold value I µ̂ Th . I µ̂ Th governs the boundary around ri to ascertain the existence of an outlier within the string current measurements. Based on Eq. (3) and the choice of I µ̂ Th , fault detection is governed by the following law:
|ri | < εTh ⇒ No fault |ri | > εTh ⇒ Fault present in ith string.
(6) th
(4)
I µ̂ Th can be estimated using the following: εTh = sup ∥ri ∥2 , under no fault condition. ∀ i ∈ [1, n]
(5)
In Eq. (5), ‘sup’ represents the supremum and I µ̂ Th , calculated as per Eq. (5), signifies the calculation of the greatest lower bound on the norm bounded values of string current measurements under no fault conditions. In practice, the choice of I µ̂ Th is governed by practical considerations such as the number of modules and strings, solar irradiation level and operating temperature of each module. Some sample values of I µ̂ Th are discussed in the simulations and experimental results sections. In the event of simultaneous occurrence of intra-string faults with multiple residuals as per Eq. (4), it is likely that a cross-string lineline fault has occurred. While the focus of this paper is to illustrate and validate intra-string line-line fault detection and location, a similar approach can be developed for the detection and location of cross-string faults. The generalised block diagram of the fault detection approach is shown in Fig. 5(a). For each string, residual (ri) indicates a fault signal based on Eqs. (1)–(5). As an example, Fig. 5(b) represents the string current accompanying intra-string faults within string 1 at various module locations (Li), i ∈ [1,4 ] for a 4 × 4 array. For each fault location (Li), the respective current magnitudes (Ii) of string 1 are changing. An increase in the fault location results in decreased Ii, which improves the sensitivity of the residual signal. As shown in Fig. 5(b), current
Fig. 4. Flowchart of the proposed fault detection and location procedure. 201
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Fig. 5. (a) An outline of the proposed fault detection system based on residual generation, (b) An example of the current vs fault location characteristics for a 4 × 4 array with fault at different locations in string 1. The figure shows substantial current variations in string 1for different fault locations, while the currents in strings 2, 3 and 4 remains unaffected.
practically meaningful because in Queensland nearly 75% – 90% of the time on a clear sky day the irradiance levels are within 400–1000 W/ m2. To obtain the regression function for Eq. (7), a 3-dimensional data set between Li–G–Ii is obtained through a series of simulations performed using a simulation model developed in MATLAB/Simulink. Various array configurations are implemented in Simulink including 4 × 4, 6 × 3 and 15 × 4. Fig. 6 shows the data set obtained for the 15 × 4 configuration. The data set is obtained by running the simulation sweep for various fault location (between 1 and 15) and Solar irradiance (between 400 W/m2 and 1000 W/m2 in the steps of 100 W/ m2) combinations and the corresponding string (fault) current levels.
faults along the string. Fig. 7 also shows that the irradiance level shifts the current versus location characteristics up or down without changing the slope of the characteristics. The threshold ( I µ̂ Th ) is calculated using Eq. (5) and absolute values of threshold with respect to irradiation are given in Table 2. Table 2 demonstrates the ability of the proposed approach to detect and locate faults. As G and Li change the relative I1 also changes. It can be clearly observed that an increase in r1 is related to a decrease in I1 which allows prompt fault detection. Once the fault is detected, it is localised by following the procedure in Section 3.2. The estimated locations ∧
(
Li ) (estimated)
with respect to actual (Li(actual) ) shows a good trade-off be-
tween accuracies. It is noteworthy that to reduce the complexity and false alarms, this analysis used irradiances of 700 W/m2 and above. The fault diagnosis accuracy is expected to increase as the PV model is optimised further.
∧
In order to obtain Li as per Eq. (7), the dataset is fitted into a regression model using MATLAB’s polynomial surface fit function (Marthworks, 2017) to obtain a regression expression of the following form: ∧
Li = C1 + (C2 × G ) + (C3 × Ii ) + (C4 × G 2) + (C5 × G × Ii )+ (C6 × Ii 2) + (C7 × G 2 × Ii ) + (C8 × G × Ii 2) + (C9 × Ii3).
4.1.2. Case study: 6 × 3 PV array The fault location estimation expression in Eq. (8) is specifically fit for the 6 × 3 PV array configuration by following the procedure outlined in section 3.2. For validation, four different irradiation levels (between 700 W/m2 and 1000 W/m2 in the steps of 100 W/m2) are considered. The intra-string faults are created along string 1 for different irradiance conditions and the corresponding string currents are measured in simulations. Fig. 8 shows the results which display a nearly linear relationship between string 1 current and fault locations. The irradiance levels simply shift the characteristics up/down without altering the slope of the characteristics. The fault detection and location results are summarised in Table 3 which confirms the ability of the
(8)
Using the simulation generated dataset, we estimate the values for parameters (C1 to C9). A cubic fit is selected to estimate coefficients of a polynomial that fits a set of data in a least-squares sense. For the 15 × 4 array configuration corresponding to Fig. 6, a cubic fit was found to be the most suitable. 4. Simulation and experimental validation The proposed approach in Section 3 is validated in this section through simulations and experiments. 4.1. Simulation results In order to test the ability of the proposed approach to detect the faults and estimate the likelihood location, a series of line-line faults within the string are used in simulation. In this section, the proposed method is applied on three PV array topologies to demonstrate its validity on different PV array configurations: 4 × 4, 6 × 3 and 15 × 4 PV arrays. 4.1.1. Case Study: 4 × 4 PV array In this case, there are four strings and each string has four series connected PV modules. The intra-string line-line faults are created under different irradiance levels as seen in Fig. 7. The faults are created at different locations along string 1. For each fault scenario, the residuals for each string are calculated and the fault location estimated using Eqs. (3) and (6), respectively. Fig. 7 shows that the fault current level through the faulty string changes linearly with the location of the
Fig. 6. Plot showing the dependence of string current on fault location and irradiance levels for 15 × 4 array. 202
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with four strings where each string has 15 series connected PV modules is simulated for analysis. Typically, 400 V bus voltage is required for a 220 V 50 Hz single-phase grid, and a 600 V bus voltage is required for a three-phase grid (Standards Australia, 2014). In this case, 15 PV modules per string are considered to maintain the array voltage within the given range. Intra-string line-line faults with different locations are created in the PV array under various irradiance levels. The string (fault) current magnitudes with estimated fault locations are represented in Fig. 9(a). With increasing number of modules, the linearity between the string current and fault location begins to somewhat diminish especially for low irradiance levels of around 700 W/m2. This trend is reflected in the accuracy of the fault location estimation (summarised in Fig. 9(b)). The linearity at the closest fault location is slightly lost due to negligible change in Ii. This change is obvious, as the string is comprised of more modules (i.e. 15 × 4), their respective change in Ii towards a fault location closer to string end is nearly constant. Considering that the irradiance levels on a near clear sky reaches above 700 W/m2 for most of the time (e.g. 9 am to 4 pm), the loss of linearity causing lower fault location accuracy is not expected to compromise the fault location performance in practical systems.
Fig. 7. String current for various fault locations and irradiation in 4 × 4 array. Table 2 Simulation validation of fault detection and location approach on 4 × 4 array. G
String Current(I1)
Detection Residual(r1)
1000 900 800 700
2.98 −2.62 −9.43 −17.62
0.60 2.62 11.50 21.44
Location
εTh
0.55 0.50 0.45 0.40
Li(actual)
1 2 3 4
Accuracy ∧ Li (estimated)
1.0 2.03 3.03 4.0
4.2. Experimental results 100% 99% 99% 100%
The approach for fault detection and location proposed in section 3 is experimentally validated on a setup shown in Fig. 3(a). Two experimental PV array configurations, 4 × 4 and 6 × 3, are developed to demonstrate the applicability of the proposed approach for different array topologies.
4.2.1. Validation on a 4 × 4 array The PV array is arranged with four strings with each string comprised of four modules connected in series. Current sensors are placed at each string’s end to analyse the impact of line-line faults on string current magnitudes. For experimentation, random intra-string faults are created by short circuits between M14 till M11 in string 1 (See Fig. 3(b)). The faults were created when the G levels at the experimental setup location were (i) 700 W/m2 and (ii) 950 W/m2. For each irradiance level, the faults are created along string 1 and the corresponding string currents are measured. The results obtained from these experiments are summarised in Table 4. The threshold is calculated using Eq. (5) under a no fault condition at a corresponding irradiance. The results in Table 4 confirm the ability of the proposed approach to accurately detect and locate intra-string line-line faults. The performance in estimating the fault location is particularly noteworthy. The proposed approach is capable of estimating the location with high level of accuracy at different irradiance levels.
Fig. 8. String currents for various fault locations and irradiation in 6 × 3 array. Table 3 Simulation validation of fault detection and location on 6 × 3 array. G
String Current(I1)
Detection Residual (r1)
εTh
Location Li(actual)
Accuracy ∧
4.2.2. Validation on a 6 × 3 array The experimental validation of the proposed approach is repeated on a 6 × 3 PV array. For evaluation, intra-string faults are introduced at various locations within string 1 sequentially from location 1 to location 6. The fault detection and location estimation results are given in Table 5. The threshold ( I µ̂ Th ), calculated using Eq. (5), is found to be 0.45 and 0.5 at 750 W/m2 and 800 W/m2 irradiance levels, respectively during fault free conditions. The results confirm that the proposed approach is capable of successfully detecting and locating faults regardless of the array configuration. The expected fault location can provide the exact fault location
Li (estimated)
1000 900 800 700 600 500
5.60 3.31 0.84 −1.91 −5.47 −8.39
0.67 1.84 3.76 6.27 9.24 13.05
0.65 0.60 0.55 0.50 0.45 0.40
1 2 3 4 5 6
0.98 1.99 3.02 4.08 5.07 6.10
98% 99% 99% 98% 98% 98%
approach to accurately detect faults and locate them in terms of the faulty module number.
∧
after rounding of the
Li values. (estimated)
Also, it is noticeable from Table 5
that the accuracy of the estimated fault location increases with an increase in experimental fault location.
4.1.3. Case Study: 15 × 4 PV array In order to make the proposed method more realistic, a PV array 203
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Fig. 9. (a) Location vs fault current characteristics for 15 × 4 array, (b) The corresponding accuracy of the proposed fault location approach.
5.1. Variation in number of modules
Table 4 Experimental results of estimated and actual fault locations with different irradiance in 4 × 4 array. G
700 700 700 700 950 950 950 950
String Current (I1)
5.10 1.70 −4.40 −12.7 1.50 −5.80 −14.1 −24.5
Detection
Location
To generalise the proposed approach as per Eq. (6), the effect of a varying number of modules (m) in a string is considered. The rest of the parameters (G, n, T) are kept unchanged. The relationship between the fault location and string current levels for different string sizes is analysed. Different arrays with a varying number of modules within each string (3 × 3, 5 × 3, 7 × 3, 9 × 3) are simulated. The string current magnitudes vs fault locations are gathered and presented in Fig. 10. From Fig. 10, it is observed that even though the number of modules in a string changes, the range of current magnitudes remains the same. With this observation, the following expression is proposed:
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.325 3.575 9.5 17.35 5.5 12.8 21.1 31.5
0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4
1 2 3 4 1 2 3 4
1.18 1.80 2.69 3.58 1.12 2.09 3.08 4.05
85% 90% 90% 90% 89% 95% 97% 99%
snew = sold ×
750 750 750 750 750 750 800 800 800 800 800 800
String Current (I1)
6.5 5.2 2.6 −2.7 −4.9 −6.5 6.7 5.2 2.1 −2.92 −5.47 −6.7
Detection
Location
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.4 0.9 3.5 8.8 11.0 12.6 0.67 1.13 4.23 9.25 11.8 12.73
0.45 0.45 0.45 0.45 0.45 0.45 0.5 0.5 0.5 0.5 0.5 0.5
1 2 3 4 5 6 1 2 3 4 5 6
1.16 2.49 2.42 4.64 5.40 6.16 1.14 2.40 3.37 4.41 5.35 6.14
(9)
where sold denotes the slope of the line which characterises current versus location characteristics, snew represents the slope corresponding to the current versus location characteristics for the new array configuration for which the fault location expression is to be remodelled, mold and mnew represent number of modules in the existing string and new string, respectively. For validation, let us consider a scenario whereby we want to obtain the fault location expression for a 6 × 3 array using an expression derived and validated for a 4 × 3 array. In this situation, we can obtain the slope of the line characterising current vs fault location using Eq. (9). Fig. 11 shows the comparison between the actual and approximated characteristics. Once snew is known, the new current (Ii(new)) for any string size can be obtained using Eq. (10).
Table 5 Experimental results of estimated and actual fault locations with different irradiance in 4 × 4 array. G
mold . mnew
86% 80% 81% 86% 93% 97% 88% 83% 89% 91% 93% 98%
Ii (new) = Ii ×
mold . mnew
(10)
where mold and mnew are number of modules in old and new array respectively. The fault location expression for a new array size can be obtained by substituting Eq. (10) in Eq. (7). The approach is validated on 6 × 3 arrays and results are revealed in Table 6. The results confirm the ability of the proposed approach to generalise for m number of modules in a string with reasonable fault location accuracy.
5. Extension of approach for any array size In section 3, the proposed approach for fault location is developed for fixed number of modules and strings. Ideally, it is desirable to extend the approach for any array size so as to enhance its portability to various PV array configurations with minimal effort. Given a fault location estimation expression for a specific array configuration, this section proposes approaches to re-model the expression to suit other PV array configurations with varying modules, strings and temperature.
5.2. Variation in number of strings The fault location approach discussed in Section 3 is considered for a varying number of strings (n) in an array using Eq. (6). The parameters (G, m, T) are kept constant to analyse fault current magnitudes with a change in string numbers. To get insight into string current (Ii) and fault location (Li), various array combinations with number of strings (4 × 3, 4 × 4, 4 × 5, 4 × 6) are simulated and plotted as shown in Fig. 12. The string current magnitudes (Ii) in Fig. 12 show a linear shift with number of parallel strings. Therefore, scaling of the string 204
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Fig. 10. Fault current magnitudes with varying number of modules in a string (a) 3 × 3, (b) 5 × 3, (c) 7 × 3, (d) 9 × 3 and locations at 1000 W/m2.
Fig. 12. Fault current magnitudes with varying string numbers at 1000 W/m2. Fig. 11. Verification of approximate and exact current characteristics for 6 × 3 array.
snew = nratio × nold × nnew .
where snew is the slope of the line which characterises current versus location of new string, nratio denotes ratio of old and new strings numbers, nold and nnew represents number of old and new strings respectively. To validate the effect of string characteristics, we want to obtain the expression for a 4 × 6 array using an expression derived and validated for a 4 × 4 array. In this case, the slope of the line characterising current vs fault location can be obtained using Eq. (11). Fig. 13 shows a trend in the string fault currents with variations in number of strings. This trend is captured in the form of actual and approximated characteristics. Once the slope of snew is known, the new string current for any string numbers in array can be obtained through Eq. (12). The expression for estimated fault location is given in Eq. (12):
Table 6 Fault location estimation in a 6 × 3 array using an expression derived from a 4 × 3 array. Irradiation(G)
1000 1000 1000 1000 1000 1000
String current (Ii)
5.60 2.15 −1.84 −6.34 −11.31 −16.77
Location
Accuracy
Li(actual)
∧ Li (estimated)
1 2 3 4 5 6
0.63 1.52 3.54 4.48 5.33 6.29
(11)
63% 76% 84% 89% 93% 95%
Ii (new) = Ii × numbers with the string current magnitudes can be used for approximating the characteristics of any number of strings. From the characteristics observed in Fig. 12, the following expression is proposed:
nnew . nold
(12)
The estimated fault location expression for the new array size can be obtained by substituting Eq. (12) in Eq. (7). The given approach is verified on 4 × 4 array and the results are shown in Table 7. The results confirm that the proposed approach is capable of generalising fault 205
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with MPPT. The results are summarised in Table 8. From the results, it can be seen that the net effect of MPPT is the overall slight drop in currents in all the strings when compared with without MPPT. This drop is caused by the voltage adjustments by MPPT to maximise power yield. Nonetheless, the relative current differential between the faulted and fault-free strings is maintained and the pattern in which fault current varies relative to fault location is the same without and with MPPT. The results confirm that the pattern of current variations relative to fault location is consistent between without and with MPPT. Consequently, the proposed method is valid for both without and with MPPT scenarios.
5.5. Discussion It is observed from the results that the change in string current has strong correlation with fault detection and location. During an intrasting fault, the short circuited path bypasses modules in the faulted string. As a result, the bypassed module does not contribute any voltage into the string. Since strings are connected in parallel, the array maintains the same voltage across all strings by reducing the current of the faulted string. As a result, the faulty string’s current is different to a normal string’s and is governed by the mismatch created through bypassed modules. At the occurrence of a fault with low mismatch (low fault location), the faulty string’s current magnitude is nearly the same as a normal string’s which results in diminishing detection accuracy. Additionally, irradiation levels during a fault influences the string current magnitude. Due to that, the residual of the faulty string in low irradiation appears close to the threshold boundary and makes fault detection cumbersome. Similarly, fault location estimation accuracy is slightly lower during low irradiation. To overcome these issues, selection of irradiation levels between 400 W/m2 and 1000 W/m2 is found sufficient for fault detection and location. However, the proposed method can still work in low irradiation conditions with a lower accuracy. The same results are observed in the other research (Mallor et al., 2017; Platon et al., 2015; Yi and Etemadi, 2017) which underperforms in low irradiation conditions. Moreover, the accuracy of the estimated fault location is exceptional when the proposed method is developed for the same array size of the system under test. However, it limits the approach being used for any size of array. To overcome this issue, the approach is extended for any array size with compromises in fault location accuracy. It is also observed that the effect of small mismatch (location) faults on string current levels is nearly the same with a large array size. For such cases, the performance of the proposed method is expected to improve if accurate measurements are taken. Besides that, most of the PV arrays are usually protected by OCPD which provides isolation if the fault current (backfeed current) reaches protection settings. However, from the results it is observed that the string current can reach to the OCPD fault current setting only when the fault location mismatch is above a 75%. Even if the OCPD detect the fault, it may not locate the fault within the string. Therefore, the proposed method has good potential in fault diagnosis in addition to the use of OCPDs. The proposed method only uses string current and irradiance, which
Fig. 13. Verification of approximate and exact current characteristics for 4 × 6 array. Table 7 Fault location estimation for 4 × 4 array using an expression derived from a 4 × 6 array. Irradiation (G)
1000 1000 1000 1000
String current (Ii)
Location
2.96 −4.64 −13.98 −25.16
Accuracy
Li(actual)
∧ Li (estimated)
1 2 3 4
1.43 2.38 3.32 4.28
69% 84% 90% 93%
location estimation for n number of strings with good accuracy. 5.3. Variation in temperature Since the temperature (T) has less dependency on the Isc of a PV module, the string current may be affected only slightly with major change in T and is therefore usually ignored. The proposed approach is verified by changing the T as per Eq. (6) and keeping other parameters (G, m, n) constant. It is observed from the simulations that the temperature change has a negligible effect on the string currents which does not affect the fault detection and location. 5.4. Effect of MPPT on proposed method In order to evaluate the effect of MPPT on the validity of the proposed method, additional simulations are performed with MPPT. In particular, in order to clarify the effects of MPPT on the proposed method, simulation analysis is performed with MPPT on the 4 × 4 array for a range of irradiance conditions. Faults are created in string 1 only, therefore, the residual values (r1) for string 1 only are shown. Different irradiance levels and fault locations are considered to encompass the effects of irradiance levels and fault locations relative to without and Table 8 Experimental results of the proposed method with MPPT effect on 4 × 4 array. G (W/m2)
Detection
Location
String current
1000 900 800 700
I1
I2
I3
I4
2.87 −2.39 −8.21 −15.56
7.62 6.98 6.14 4.89
7.62 6.98 6.14 4.89
7.62 6.98 6.14 4.89
Accuracy
Residual (r1)
εTh
Li(actual)
∧ Li (estimated)
0.55 0.50 0.45 0.40
0.31 0.33 0.34 0.36
1 2 3 4
0.97 2.14 3.36 4.18
206
97% 93% 89% 96%
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References
are usually available in PV plants with string monitoring systems. The string currents during fault have a nearly linear relationship with fault location, which makes the proposed method scalable. In practice, the proposed algorithm can be implemented in a microcontroller or programmable logic controller (PLC) to monitor the string currents and diagnose the fault within PV arrays. The speed of fault detection and location is governed by the computational time required for the execution of the proposed method. The computational time is likely to substantially increase with the size of solar farms as the number of strings increases. Consequently, the field implementation of the proposed method will involve the use of cloud based platforms with high computational capabilities. Nonetheless, the delay caused due to longer computational times is likely to have minimal effect on the main feature of the proposed method which is to perform automatic on-line fault diagnostics. Another important factor is the consideration of partial shading. Consistent with the standard practice in utility scale solar farms, it is assumed that there will be no partial shading in utility-scale solar farms unless moving clouds create the partial shading. The delay in the speed of the fault diagnostics due to partial shading corresponds to the duration of the partial shading that result in irradiance levels below 400 W/m2. Whist this delay in practical solar farms is likely to be inconsequential, thorough analysis of the effects of partial shading on the proposed method is essential and remains a topic for future research.
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6. Conclusions The proposed fault detection and location algorithm for a PV system is based on the residual generation and regression coefficient extraction. The results successfully demonstrate the ability of the proposed method to successfully detect and locate intra-string line-line faults that otherwise may remain undetected in large-scale solar farms. The method uses residual based outlier detection governed by the pre-defined threshold. Any outlier outside the boundary of the threshold indicates a fault. Once the fault is detected, the fault location algorithm estimates the fault location within the string. The fault location algorithm uses regression based expressions which requires only irradiance and string current magnitudes. Both theoretical simulations and experimental validations are carried out, which have shown promising performance of the proposed method. Further, the fault location approach has been generalised and validated for any size of array. Through the analysis it is found that string level monitoring can significantly enhance the visibility on the dc side of the PV plants regardless of the plant size. Noting that the existing protection devices are primarily meant to protect inverters and dc side fires, the proposed approach complements the existing protection equipment by prompt detection and location of PV faults, eventually benefitting the longevity of PV modules. A natural extension of the proposed approach is to make the proposed approach adaptive to variations in module degradation condition. This work will be undertaken in the near future.
Acknowledgements The proposed fault detection and location approach was submitted to IP Australia on 31st October 2018 as an Australian Provisional Patent (No. 2018904145). This work was performed in part or in full using equipment and infrastructure funded by the Australian Federal Government’s Department of Education AGL Solar PV Education Investment Fund Research Infrastructure Project. The University of Queensland is the Lead Research Organization in partnership with AGL, First Solar and the University of New South Wales. The authors gratefully acknowledge the Ministry of Social Justice and Welfare, Maharashtra State, Government of India for a Doctoral Fellowship (Amit Dhoke, No. DSW/Edu/2014-2015/D-IV/114). 207
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