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Wavelet transform-based fault detection method for hydrogen energy-based distributed generators
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Wavelet transform-based fault detection method for hydrogen energy-based distributed generators €kay Bayrak* Go Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, 16330, Bursa, Turkey
article info
abstract
Article history:
Integration of hydrogen energy-based distributed generators (HEBDGs) to the main grid
Received 31 March 2018
causes some power quality (PQ) problems. The conventional fault detection methods fail to
Received in revised form
detect some PQ disturbances, thus intelligent methods are proposed in the literature
20 June 2018
recently. This paper focuses on developing a wavelet transform (WT)-based fault detection
Accepted 28 June 2018
method for HEBDG systems to detect PQ disturbances in the low-voltage grid connection.
Available online xxx
The proposed method uses the discrete WT and Daubechies wavelets of order 4 to detect the voltage swell, voltage sag, voltage interruption and transient disturbances in a HEBDG
Keywords:
system. The performance of the WT-based method is also compared with the conventional
Wavelet transform
fault detection methods, and using the time-frequency domain analysis by WT increases
Hydrogen energy
the stability of the proposed method. The results verify that the conventional methods are
Distributed generation
not capable of detecting some PQ disturbances properly, but the proposed WT-based
PQ disturbances
method is more reliable according to the conventional methods. © 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction The traditional power distribution systems have unidirectional power flow from high voltage to low voltage, but the grid has been transformed having bi-directional power flows by integration distributed generation (DG) systems. As a consequence of that, the classical analysis, operation, and design methods are not capable of managing this complex grid structure. Besides, the consumers have a significant effect on the quality and the sustainability of the power supplied by the grid. The hydrogen energy-based distributed generation (HEBDG) systems are also expected not to operate out of the related standards. The integration of DGs into the grid is an essential issue for a sustainable power flow between the grid and the consumers.
Thus, HEBDG systems must be adapted to the defined electrical rules in IEEE 929-2008 to provide the steady-state operation at the grid connection point. The power quality is required for HEBDG systems to provide the reliability and the stability of the grid. Fig. 1 shows the general schematic of a grid-connected HEBDG system. The most of the distribution companies in the world define their grid connection standards, and these standards are called the grid code. There are also some international norms for consideration of the grid connection. Threshold values of frequency and voltage are defined in IEEE 929-2008 [13]. The required opening time for circuit breakers (CBs) is also important for fault detection. The frequency and the voltage change from the nominal values in a fault condition. Thus, a circuit breaker (CB) is required for disconnection of the grid. Voltage shutdown,
* Corresponding author. E-mail addresses: [email protected], [email protected]. https://doi.org/10.1016/j.ijhydene.2018.06.183 0360-3199/© 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 1 e The general schematic of a grid-connected HEBDG system.
short-circuit and equipment failures are some reasons causing power quality disturbances in a DG system [17]. The grid loses the synchronization in a fault condition and the power system stability is significantly different from the normal operation of DG. The voltage and the frequency change from grid reference values and as a result, a HEBDG system could be damaged in a PQ disturbance condition. Also, the working staffs in the DG system are life-critical in any fault conditions. For example, a HEBDG system is required to immediately disconnect from the grid in an islanding situation. In general, a circuit breaker is switched by triggering a generated control signal to detect islanding properly.
Conventional fault detection methods in HEBDG systems The non-detection zone (NDZ) and the quality factor (Qf) are significant design parameters for designing conventional fault detection methods. Fault detection is not possible in NDZ, thus performing a small NDZ is important for a DG system to provide a reliable DG operation. NDZ depends on local load in inverter resident fault detection (passive and active methods) methods [26]. The probability of realizing the fault decreases when the active power of load and active power of DG equals each other. This condition is valid when the load and the grid frequencies are matching [27,28]. Reactive power mismatch ðDQÞ and active power mismatch ðDPÞ are the main fault detection decision parameters for conventional methods. DP and DQ are useful to specify the NDZ boundaries in this method. The limitations of NDZ could be determined by using the Eq. (1) [29,30].
V Vmax
2 1
DP P1
V Vmin
system frequency is the range of ðfg H%1Þ, the change of voltage amplitude is negligible. Thus, (UVP/OVP) method is not capable of detecting the fault in this situation. However, (UFP/OFP) method is capable of detecting the fault in this situation [25]. Therefore, (UFP/OFP) method is more effective than (UVP/OVP) method in small power mismatches.
Intelligent fault detection methods in HEBDG systems Intelligent methods used to detect power quality and fault events can be examined under four main headings: Fuzzybased event detection methods, event detection methods based on estimation (estimation), classification based event detection methods, and field transformation-based methods. Fig. 3 shows the methods used to identify faults and power quality events in different components of a grid-connected HEBDG system [5]. In order to overcome the difficulties of threshold-based fault detection methods, fuzzy logic-based methods have also been used in the literature. These methods use knowledge-based logic obtained from experiments and observations by creating logical rules to detect errors. Fuzzy logic was implemented using a model-based approach for fault detection in cabling, transmission lines [10] and PV inverters [11]. In one of the studies performed using fuzzy logic-based methods, the type of failure that occurred in a diesel generator and the type and size of the fault were estimated. In another study [9], fuzzy logic based methods were applied
2 1
(1)
Vmax is the maximum threshold value of the voltage that is defined by grid code and Vmin is the minimum threshold value of the voltage that is defined by grid code in Eq. (1). Fig. 2 shows the NDZ and it is large and the set threshold values are complex in passive fault detection methods. Thus, if the power system is in a power balance, these methods are not successful for detecting any faults [31,32]. NDZ is smaller than passive methods in active methods but NDZ is not zero in active methods. Under/over voltage protection (UVP/OVP) and under/over frequency protection (UFP/OFP) methods are the passive fault detection methods, and they are defined as fundamental protection methods for fault detection [24]. If the change of
Fig. 2 e NDZ for conventional fault detection methods.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 3 e Intelligent fault detection methods in HEBDG systems.
using voltage and current measurements to detect faults in wind turbines. Defects in distributed manufacturing systems are particularly difficult to detect using raw sensor data. For this reason, data preprocessing steps are required to define some important features that may enhance the discrimination between the data. By specifying the suitable properties, the data obtained from some faults are placed in separate clusters. This feature allows trained classifiers in the field to diagnose faults. These properties are determined using the differences between the actual and theoretical values. Attribute extraction can be applied to model-based systems as well as data-centric systems derived from real systems. Kurz et al. [6] found fuelcell failures in their work. Field-transform-based methods use time-frequency components of the signal to detect fault and power quality events, unlike other methods. These methods can be examined in two parts, Fourier transforms based methods and Wavelet Transform (WT) based methods [1,2]. Fourier Transforms (FT) is used to classify faults by performing spectrum analysis of
steady-state signals. Fast Fourier Transform (FFT) is also used in wind turbines. The biggest advantage of the WT method over other fieldtransformed methods is that it locally analyzes the signal in the time-scale domain. The analysis with WT is more flexible since it has a variable window size according to the analysis made with short time FT. PQ can include event markers as well as high frequency and low-frequency disturbing signals. It will be more accurate to analyze the PQ event signals by the WT method instead of the FT and short time FT method because of the different frequency signal components they contain. In a study in which a WT-based voltage collapse/bounce detection algorithm was proposed, two hybrid Daubechies waves were applied to the same signal to create a hybrid structure [3]. This algorithm applied to the signals obtained from the simulation results by the PSCAD/EMTDC analysis program proved to be faster and more reliable than the proposed algorithm compared with methods such as HFD and d-q transformation.
Fig. 4 e PEM FC stack model [17].
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 5 e The general schematic of the conventional fault detection model.
In another study, WT-based method for determining island mode operation in wind turbines is proposed, an algorithm has been proposed that allows the voltage signal received through the data acquisition card to be subjected to 5-level discrete wavelet transform (DWT) to switch to island mode according to the threshold value set for the d5 coefficient [4,7]. In the study of grid-connected PV system model using Matlab/ Simulink environment using PowerSim toolbox, the current embedded in PV inverter and current detection by WT technique were performed [8].
The commercial inverters used in the HEBDG systems have usually active and passive PQ disturbance detection methods for fault detection. Thus, selecting and modeling inverter resident fault detection methods is an essential issue for designing and testing HEBDG systems. An intelligent WTbased fault detection method for HEBDG systems is proposed for PQ disturbances in the paper. The proposed method uses the discrete WT to detect the PQ disturbances like voltage interruption, voltage sag, voltage swell and transient conditions in a HEBDG system. The proposed time-frequency
Fig. 6 e Determining coefficients of the 3-level WT. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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domain analysis by using WT increases the stability of the proposed method. Also, active and passive fault detection methods (inverter resident methods) are modeled for HEBDG systems and the performance assessment of these methods is investigated. The results verify that the conventional methods are not capable of detecting some PQ disturbances properly, but the proposed WT-based fault detection method is more reliable according to the conventional methods.
the worst scenario for inverter resident methods, thus the load is modeled as an RLC load to assess properly the performance of the developed model. The transfer function of RLC load is used in the model and Eq. (2) presents this transfer function. The inverter current is defined in Eq. (7) is used in developed PJD model [14e16]. 1 1 1 RLCs2 þ Ls þ R du ¼ þ þ Cs ¼ s ¼ GðsÞ Z R Ls RLs2 dt
(2)
Modeling fault detection methods for Hebdg systems
Start
Modeling PEM fuel cell (FC) stack A PEM FC stack model is used in the developed model. This model consists of an anode, a cathode, electrolyte layer and the gas flow channels. Hydrogen, oxygen, steam pressure and the current density are the inputs of an FC stack. The output voltage is the sum of the number of FC. The developed model of the PEM FC stack is indicated in Fig. 4.
Acquire Signal
Modeling conventional fault detection methods Wavelet transform-based PQ disturbance detection method, active and passive fault detection methods are investigated to evaluate the performance assessment of these methods for HEBDG systems. Fig. 5 shows the general schematic of developed model in Matlab Simulink. The proposed model has four main sub-models called as inverter current hysteresis model, load model, frequency and voltage detection model, and the investigated fault detection methods.
DWT Method
Modeling UVP/OVP & UFP/OFP fault detection methods The grid reference values are out of nominal values in a fault condition, and the HEBDG system is required to disconnect from the grid immediately. Short-circuits, equipment failures and voltage shut down are some faults that could cause faults in a HEBDG system. Passive methods are the main protection techniques for fault detection in the HEBDG systems [12]. Under/over voltage protection (UVP/OVP) and under/over frequency protection (UFP/OFP) protection methods are the fundamental protection methods for a microgrid [22,23]. The grid-connected inverters in DGs must have at least UVP/OVP & UFP/OFP protections to avoid faults according to IEEE 9292008. Fig. 5 shows the developed model of UVP and OVP methods. This model checks the grid voltage and triggers the circuit breaker when the defined threshold values are out of the reference values.
Modeling phase jump detection (PJD) method The phase jump detection method (PJD) is another inverter resident passive method that follows the phase angle between the inverter output current (Iinverter) and the voltage (Vinverter). The phase angle changes immediately in a fault condition, thus the fault is detected. PJD method runs effectively only in unique power factor, thus the performance is not suitable when the power factor changes. The developed PJD model uses an RLC load to define the load effect on the HEBDG system. The resonance condition is
Decompsion to level 4
Threshold>d1
YES NO Power Quality Disturbance Detecon Fig. 7 e The flowchart of the proposed method by WT.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Slip mode frequency shift (SMS) method Active methods use a disturbance signal injecting it to the main signal and the difference in the distribution signal is measured to detect the fault, differing from passive methods [18e20]. The difference of disturbance signal is very limited in grid connection, but when the grid is disconnected a significant difference occurs in the disturbance signal. The phase change in the inverter also affects the grid frequency. The SMS method uses a positive feedback and it changes the phase angle of the inverter current when a difference occurs in the grid frequency [21]. The phase angle is defined as in Eq. (3). " # p f ðk1Þ fn q ¼ qm sin 2 fm fn
(3)
In Eq. (3), qm is the maximum phase angle, fn is the nominal frequency of DG system and f ðk1Þ is the frequency in the previous period.
The proposed discrete WT-Based fault detection method The WT method is a signal processing method with the ability to process data at different scales and resolutions. The WT method can locally investigate the discontinuities in highorder derivatives and the sudden changes in the signal where other signal processing methods are insufficient to detect power quality events. In the WT method, since the event signal can be examined in the amplitude-frequency domain, the time information of the sign disappears. For this reason, while WT produces
Fig. 8 e (a) Grid voltage, (b) the load current for PJD method in a voltage swell condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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positive results in stationary signals such as harmonic generation, it is not be used for short-term voltage transients and transients. Short-time FT makes it impossible to analyze both the low-frequency and high-frequency components of the signal at the same time, since the scale used for windowing is constant, although it results in non-stationary signals [1]. The calculated wavelet coefficients most closely approximate the sign wavelet at the current scale. If there are major components of the frequency of the mark on a given scale, the wavelet will be very close to the work in this current frequency domain. The calculated wavelet coefficients will be large in time e scale plane in relation to each other. The signal is converted to discrete time in WT. Since the continuous WT (CWT) method requires a lot of processing load, applications use Discrete WT (DWT) [2] and
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Multiplication-type DWT [3]. Using the wavelet function, the DWT equation (4) is computed. DWTðs; bÞ ¼ 2s=2
Z
f ðtÞj 2s t b dt
(4)
Where; s is the scale quantity (frequency) and b value is the displacement (time) value. The amount of scale change when converting DWT is of great importance in terms of the accuracy of the analysis. It should not be forgotten that choosing a small amount of scale will slow down the process speed, and large selection will decrease the frequency resolution. In the multi-resolution analysis method, the approach component obtained by passing the signal through the lowpass filter can be subjected to low-pass and high-pass filtering again to obtain the frequency band range desired to
Fig. 9 e (a) Grid current and, (b) the load current for PJD method in a frequency change condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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be examined. The detail and approximation coefficients of the 3-level wavelet transform applied and the amount of sample at each level are given in Fig. 6. After the signal is passed through the low-pass and highpass filters, the pair is sub-sampled and the resolution is halved in proportion to the number of samples. The frequency resolution doubles because the frequency band of the marking covers half of the previous frequency band. This process is repeated and the number of repetitions indicates the stream of WT.
The flowchart of the proposed method by WT is shown in Fig. 7. The proposed method uses the Daubechies wavelets of order 4 (db4) to detect the voltage interruption, voltage sag, voltage swell and transient disturbances in a HEBDG system.
Results The developed model is simulated under different grid conditions. This section presents obtained results from different
Fig. 10 e (a) Frequency, (b) grid voltage for PJD method in limited frequency change condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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operating conditions of developed fault detection methods for a HEBDG system.
Operating the HEBDG system with conventional fault detection methods Case 1: Voltage Swell Detection with Phase Jump Detection (PJD) Method: The proposed system is operated with PJD method. The frequency is stable at 50 Hz but the grid voltage increases from 220 V to 280 V. In this condition, the PJD method is capable of detecting the voltage swell. Fig. 8 shows the grid voltage and the load current. Case 2: Frequency Change Detection with PJD Method: The proposed system is also operated with PJD method in a frequency change. The grid voltage is stable at 220 V but the
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system frequency increases from 50 Hz to 51 Hz. In this condition, the PJD method is capable of detecting the fault and Fig. 9 shows the grid current and the load current. The load current is zero after the frequency change is detected. Case 3: Limited Frequency Change Detection with PJD Method: PJD method is also investigated in limited frequency changes. The grid voltage is stable at 150 V (Vpeak) but the system frequency increases from 60 Hz to 60.1 Hz and then decreases from 60 Hz to 59.95 Hz. In this condition, the PJD method is not capable of detecting the fault and Fig. 10 shows the frequency change and the grid voltage. PJD is not successful to detect the fault when the changes of defined threshold values are very limited. Case 4: Voltage Interruption Detection with Slip Mode Frequency Shift (SMS) Method: The developed system is also
Fig. 11 e (a) Grid voltage, (b) the load current for SMS method in a voltage interruption condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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operated with the SMS method in an active power change to detect the voltage interruption in a HEBDG system. The active power of load is adjusted to 0,9* Pinverter. In this condition, the SMS method is capable of detecting the voltage interruption event. Fig. 11 shows the grid voltage and the load current in a voltage interruption condition. Case 5: Islanding Detection with Slip Mode Frequency Shift (SMS) Method: The active power of the load is adjusted to 0,5* Pinverter. In this condition, the SMS method is also capable of detecting islanding. Fig. 12 shows the grid voltage and the load current and the load current is zero after islanding occurred. Also, islanding detection time is short compared to load power is adjusting to 0,5* Pinverter. These results show when the active power mismatch is high between the load and the grid,
detection time will be shorter. PJD method is not capable of detecting islanding in the non-detection zone (NDZ), thus the islanding detection is not possible in defined threshold values restricted by NDZ. The UVP/OVP & UFP/OFP methods have a large NDZ that covers all of the selected areas, and fault detection is not possible in this area. Also, PJD method has a smaller NDZ according to UVP/OVP & UFP/OFP methods. SMS method has the smallest NDZ comparing with other conventional fault detection methods. It is clear that SMS method is more reliable for fault detection compared to other conventional methods. Consequently, none of the inverter resident methods present a certain solution to detect reliably the faults in a HEBDG system.
Fig. 12 e (a) Grid voltage, (b) the load current for SMS method in an islanding condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Operating the HEBDG system with proposed WT-Based fault detection method The HEBDG system is operated with proposed WT-based PQ detection method. The frequency is stable at 50 Hz and the sampling frequency is selected as 10 kHz in the study. The proposed method uses the Daubechies wavelets of order 4 (db4) to detect the different PQ disturbances in a HEBDG system. Case 1: Voltage Sag Detection with Proposed WT-Based Method: In this condition, there is a voltage sag disturbance
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in the HEBDG system. The voltage sag condition that is shown in Fig. 13 starts at the 0.2nd second and finalize at the 0.4th second. The amplitude of the voltage signal is less than 30% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 13, and the voltage sag disturbance is clearly detected in the system thanks to the proposed method. Case 2: Voltage Swell Detection with Proposed WT-Based Method: The developed microgrid system is performed for a voltage swell condition. This condition that is shown in Fig. 14
Fig. 13 e The voltage sag detection by proposed WT method. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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is detected in the study, and it starts at the 0.15th second and finalizes at the 0.33rd second. The amplitude of the voltage signal is more than 20% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 14, and the voltage swell disturbance is detected in the system properly.
Case 3: Voltage Interruption Detection with Proposed WTBased Method: Voltage interruption detection is a significant issue for a HEBDG system. The developed microgrid system is performed for this condition by the developed WT-based method. The voltage interruption condition that is shown in Fig. 15 is detected in the study, and it starts at the 0.13th second and finalizes at the 0.33rd second. The amplitude of
Fig. 14 e The voltage swell detection by proposed WT method. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 15 e The voltage interruption detection by proposed WT method.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 16 e Detection transients in voltage signal by proposed WT method.
the voltage signal is the 1% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 15, and the voltage interruption disturbance is clearly detected in the system. Case 4: Detection Transients with Proposed WT-Based Method: The transients in a HEBDG system are also a significant issue. The transient condition that is shown in
Fig. 16 is detected in the study, and it starts at the 0.2nd second and finalizes at the 0.205th second. The amplitude of the voltage signal is the 180% of the nominal voltage and the frequency of the transient is 750 Hz between these seconds. The decomposition coefficients and the approximation coefficient of the WT are shown in Fig. 16, and the transient disturbance is immediately detected in the system.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Conclusion [8]
A wavelet transform-based PQ disturbance detection method and the conventional fault detection methods are modeled for a HEBDG system and the performance assessment of these methods is presented in the paper. The implemented WT-based model is compared with fundamental inverter resident fault detection methods. The compatibility of these methods with the HEBDG systems could be performed easily thanks to the developed model. The phase jump detection method, over/under frequency method, over/under voltage method and slip mode frequency shift method are modeled and investigated for HEBDG systems. The generally used passive and active fault detection methods that are known as inverter resident methods are selected to assess the performance of these methods in the HEBDG systems. The proposed WT-based model has a flexible structure, so it is more suitable to implement with other conventional methods by integrating to the HEBDG systems. The proposed WT-based method uses both the frequency and the time domain analysis, thus the voltage changes in the grid signal could be detected easily according to the conventional fault detection methods. The transient detection is very difficult for conventional methods, but the WTbased method detects the limited frequency changes ontime. The results also verify that the proposed method is practical for the HEBDG systems to provide a safety connection to the grid.
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Appendix A. Supplementary data
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Supplementary data related to this article can be found at https://doi.org/10.1016/j.ijhydene.2018.06.183. [20]
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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Wavelet transform-based fault detection method for hydrogen energy-based distributed generators €kay Bayrak* Go Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, 16330, Bursa, Turkey
article info
abstract
Article history:
Integration of hydrogen energy-based distributed generators (HEBDGs) to the main grid
Received 31 March 2018
causes some power quality (PQ) problems. The conventional fault detection methods fail to
Received in revised form
detect some PQ disturbances, thus intelligent methods are proposed in the literature
20 June 2018
recently. This paper focuses on developing a wavelet transform (WT)-based fault detection
Accepted 28 June 2018
method for HEBDG systems to detect PQ disturbances in the low-voltage grid connection.
Available online xxx
The proposed method uses the discrete WT and Daubechies wavelets of order 4 to detect the voltage swell, voltage sag, voltage interruption and transient disturbances in a HEBDG
Keywords:
system. The performance of the WT-based method is also compared with the conventional
Wavelet transform
fault detection methods, and using the time-frequency domain analysis by WT increases
Hydrogen energy
the stability of the proposed method. The results verify that the conventional methods are
Distributed generation
not capable of detecting some PQ disturbances properly, but the proposed WT-based
PQ disturbances
method is more reliable according to the conventional methods. © 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction The traditional power distribution systems have unidirectional power flow from high voltage to low voltage, but the grid has been transformed having bi-directional power flows by integration distributed generation (DG) systems. As a consequence of that, the classical analysis, operation, and design methods are not capable of managing this complex grid structure. Besides, the consumers have a significant effect on the quality and the sustainability of the power supplied by the grid. The hydrogen energy-based distributed generation (HEBDG) systems are also expected not to operate out of the related standards. The integration of DGs into the grid is an essential issue for a sustainable power flow between the grid and the consumers.
Thus, HEBDG systems must be adapted to the defined electrical rules in IEEE 929-2008 to provide the steady-state operation at the grid connection point. The power quality is required for HEBDG systems to provide the reliability and the stability of the grid. Fig. 1 shows the general schematic of a grid-connected HEBDG system. The most of the distribution companies in the world define their grid connection standards, and these standards are called the grid code. There are also some international norms for consideration of the grid connection. Threshold values of frequency and voltage are defined in IEEE 929-2008 [13]. The required opening time for circuit breakers (CBs) is also important for fault detection. The frequency and the voltage change from the nominal values in a fault condition. Thus, a circuit breaker (CB) is required for disconnection of the grid. Voltage shutdown,
* Corresponding author. E-mail addresses: [email protected], [email protected]. https://doi.org/10.1016/j.ijhydene.2018.06.183 0360-3199/© 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 1 e The general schematic of a grid-connected HEBDG system.
short-circuit and equipment failures are some reasons causing power quality disturbances in a DG system [17]. The grid loses the synchronization in a fault condition and the power system stability is significantly different from the normal operation of DG. The voltage and the frequency change from grid reference values and as a result, a HEBDG system could be damaged in a PQ disturbance condition. Also, the working staffs in the DG system are life-critical in any fault conditions. For example, a HEBDG system is required to immediately disconnect from the grid in an islanding situation. In general, a circuit breaker is switched by triggering a generated control signal to detect islanding properly.
Conventional fault detection methods in HEBDG systems The non-detection zone (NDZ) and the quality factor (Qf) are significant design parameters for designing conventional fault detection methods. Fault detection is not possible in NDZ, thus performing a small NDZ is important for a DG system to provide a reliable DG operation. NDZ depends on local load in inverter resident fault detection (passive and active methods) methods [26]. The probability of realizing the fault decreases when the active power of load and active power of DG equals each other. This condition is valid when the load and the grid frequencies are matching [27,28]. Reactive power mismatch ðDQÞ and active power mismatch ðDPÞ are the main fault detection decision parameters for conventional methods. DP and DQ are useful to specify the NDZ boundaries in this method. The limitations of NDZ could be determined by using the Eq. (1) [29,30].
V Vmax
2 1
DP P1
V Vmin
system frequency is the range of ðfg H%1Þ, the change of voltage amplitude is negligible. Thus, (UVP/OVP) method is not capable of detecting the fault in this situation. However, (UFP/OFP) method is capable of detecting the fault in this situation [25]. Therefore, (UFP/OFP) method is more effective than (UVP/OVP) method in small power mismatches.
Intelligent fault detection methods in HEBDG systems Intelligent methods used to detect power quality and fault events can be examined under four main headings: Fuzzybased event detection methods, event detection methods based on estimation (estimation), classification based event detection methods, and field transformation-based methods. Fig. 3 shows the methods used to identify faults and power quality events in different components of a grid-connected HEBDG system [5]. In order to overcome the difficulties of threshold-based fault detection methods, fuzzy logic-based methods have also been used in the literature. These methods use knowledge-based logic obtained from experiments and observations by creating logical rules to detect errors. Fuzzy logic was implemented using a model-based approach for fault detection in cabling, transmission lines [10] and PV inverters [11]. In one of the studies performed using fuzzy logic-based methods, the type of failure that occurred in a diesel generator and the type and size of the fault were estimated. In another study [9], fuzzy logic based methods were applied
2 1
(1)
Vmax is the maximum threshold value of the voltage that is defined by grid code and Vmin is the minimum threshold value of the voltage that is defined by grid code in Eq. (1). Fig. 2 shows the NDZ and it is large and the set threshold values are complex in passive fault detection methods. Thus, if the power system is in a power balance, these methods are not successful for detecting any faults [31,32]. NDZ is smaller than passive methods in active methods but NDZ is not zero in active methods. Under/over voltage protection (UVP/OVP) and under/over frequency protection (UFP/OFP) methods are the passive fault detection methods, and they are defined as fundamental protection methods for fault detection [24]. If the change of
Fig. 2 e NDZ for conventional fault detection methods.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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3
Fig. 3 e Intelligent fault detection methods in HEBDG systems.
using voltage and current measurements to detect faults in wind turbines. Defects in distributed manufacturing systems are particularly difficult to detect using raw sensor data. For this reason, data preprocessing steps are required to define some important features that may enhance the discrimination between the data. By specifying the suitable properties, the data obtained from some faults are placed in separate clusters. This feature allows trained classifiers in the field to diagnose faults. These properties are determined using the differences between the actual and theoretical values. Attribute extraction can be applied to model-based systems as well as data-centric systems derived from real systems. Kurz et al. [6] found fuelcell failures in their work. Field-transform-based methods use time-frequency components of the signal to detect fault and power quality events, unlike other methods. These methods can be examined in two parts, Fourier transforms based methods and Wavelet Transform (WT) based methods [1,2]. Fourier Transforms (FT) is used to classify faults by performing spectrum analysis of
steady-state signals. Fast Fourier Transform (FFT) is also used in wind turbines. The biggest advantage of the WT method over other fieldtransformed methods is that it locally analyzes the signal in the time-scale domain. The analysis with WT is more flexible since it has a variable window size according to the analysis made with short time FT. PQ can include event markers as well as high frequency and low-frequency disturbing signals. It will be more accurate to analyze the PQ event signals by the WT method instead of the FT and short time FT method because of the different frequency signal components they contain. In a study in which a WT-based voltage collapse/bounce detection algorithm was proposed, two hybrid Daubechies waves were applied to the same signal to create a hybrid structure [3]. This algorithm applied to the signals obtained from the simulation results by the PSCAD/EMTDC analysis program proved to be faster and more reliable than the proposed algorithm compared with methods such as HFD and d-q transformation.
Fig. 4 e PEM FC stack model [17].
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Fig. 5 e The general schematic of the conventional fault detection model.
In another study, WT-based method for determining island mode operation in wind turbines is proposed, an algorithm has been proposed that allows the voltage signal received through the data acquisition card to be subjected to 5-level discrete wavelet transform (DWT) to switch to island mode according to the threshold value set for the d5 coefficient [4,7]. In the study of grid-connected PV system model using Matlab/ Simulink environment using PowerSim toolbox, the current embedded in PV inverter and current detection by WT technique were performed [8].
The commercial inverters used in the HEBDG systems have usually active and passive PQ disturbance detection methods for fault detection. Thus, selecting and modeling inverter resident fault detection methods is an essential issue for designing and testing HEBDG systems. An intelligent WTbased fault detection method for HEBDG systems is proposed for PQ disturbances in the paper. The proposed method uses the discrete WT to detect the PQ disturbances like voltage interruption, voltage sag, voltage swell and transient conditions in a HEBDG system. The proposed time-frequency
Fig. 6 e Determining coefficients of the 3-level WT. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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domain analysis by using WT increases the stability of the proposed method. Also, active and passive fault detection methods (inverter resident methods) are modeled for HEBDG systems and the performance assessment of these methods is investigated. The results verify that the conventional methods are not capable of detecting some PQ disturbances properly, but the proposed WT-based fault detection method is more reliable according to the conventional methods.
the worst scenario for inverter resident methods, thus the load is modeled as an RLC load to assess properly the performance of the developed model. The transfer function of RLC load is used in the model and Eq. (2) presents this transfer function. The inverter current is defined in Eq. (7) is used in developed PJD model [14e16]. 1 1 1 RLCs2 þ Ls þ R du ¼ þ þ Cs ¼ s ¼ GðsÞ Z R Ls RLs2 dt
(2)
Modeling fault detection methods for Hebdg systems
Start
Modeling PEM fuel cell (FC) stack A PEM FC stack model is used in the developed model. This model consists of an anode, a cathode, electrolyte layer and the gas flow channels. Hydrogen, oxygen, steam pressure and the current density are the inputs of an FC stack. The output voltage is the sum of the number of FC. The developed model of the PEM FC stack is indicated in Fig. 4.
Acquire Signal
Modeling conventional fault detection methods Wavelet transform-based PQ disturbance detection method, active and passive fault detection methods are investigated to evaluate the performance assessment of these methods for HEBDG systems. Fig. 5 shows the general schematic of developed model in Matlab Simulink. The proposed model has four main sub-models called as inverter current hysteresis model, load model, frequency and voltage detection model, and the investigated fault detection methods.
DWT Method
Modeling UVP/OVP & UFP/OFP fault detection methods The grid reference values are out of nominal values in a fault condition, and the HEBDG system is required to disconnect from the grid immediately. Short-circuits, equipment failures and voltage shut down are some faults that could cause faults in a HEBDG system. Passive methods are the main protection techniques for fault detection in the HEBDG systems [12]. Under/over voltage protection (UVP/OVP) and under/over frequency protection (UFP/OFP) protection methods are the fundamental protection methods for a microgrid [22,23]. The grid-connected inverters in DGs must have at least UVP/OVP & UFP/OFP protections to avoid faults according to IEEE 9292008. Fig. 5 shows the developed model of UVP and OVP methods. This model checks the grid voltage and triggers the circuit breaker when the defined threshold values are out of the reference values.
Modeling phase jump detection (PJD) method The phase jump detection method (PJD) is another inverter resident passive method that follows the phase angle between the inverter output current (Iinverter) and the voltage (Vinverter). The phase angle changes immediately in a fault condition, thus the fault is detected. PJD method runs effectively only in unique power factor, thus the performance is not suitable when the power factor changes. The developed PJD model uses an RLC load to define the load effect on the HEBDG system. The resonance condition is
Decompsion to level 4
Threshold>d1
YES NO Power Quality Disturbance Detecon Fig. 7 e The flowchart of the proposed method by WT.
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Slip mode frequency shift (SMS) method Active methods use a disturbance signal injecting it to the main signal and the difference in the distribution signal is measured to detect the fault, differing from passive methods [18e20]. The difference of disturbance signal is very limited in grid connection, but when the grid is disconnected a significant difference occurs in the disturbance signal. The phase change in the inverter also affects the grid frequency. The SMS method uses a positive feedback and it changes the phase angle of the inverter current when a difference occurs in the grid frequency [21]. The phase angle is defined as in Eq. (3). " # p f ðk1Þ fn q ¼ qm sin 2 fm fn
(3)
In Eq. (3), qm is the maximum phase angle, fn is the nominal frequency of DG system and f ðk1Þ is the frequency in the previous period.
The proposed discrete WT-Based fault detection method The WT method is a signal processing method with the ability to process data at different scales and resolutions. The WT method can locally investigate the discontinuities in highorder derivatives and the sudden changes in the signal where other signal processing methods are insufficient to detect power quality events. In the WT method, since the event signal can be examined in the amplitude-frequency domain, the time information of the sign disappears. For this reason, while WT produces
Fig. 8 e (a) Grid voltage, (b) the load current for PJD method in a voltage swell condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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positive results in stationary signals such as harmonic generation, it is not be used for short-term voltage transients and transients. Short-time FT makes it impossible to analyze both the low-frequency and high-frequency components of the signal at the same time, since the scale used for windowing is constant, although it results in non-stationary signals [1]. The calculated wavelet coefficients most closely approximate the sign wavelet at the current scale. If there are major components of the frequency of the mark on a given scale, the wavelet will be very close to the work in this current frequency domain. The calculated wavelet coefficients will be large in time e scale plane in relation to each other. The signal is converted to discrete time in WT. Since the continuous WT (CWT) method requires a lot of processing load, applications use Discrete WT (DWT) [2] and
7
Multiplication-type DWT [3]. Using the wavelet function, the DWT equation (4) is computed. DWTðs; bÞ ¼ 2s=2
Z
f ðtÞj 2s t b dt
(4)
Where; s is the scale quantity (frequency) and b value is the displacement (time) value. The amount of scale change when converting DWT is of great importance in terms of the accuracy of the analysis. It should not be forgotten that choosing a small amount of scale will slow down the process speed, and large selection will decrease the frequency resolution. In the multi-resolution analysis method, the approach component obtained by passing the signal through the lowpass filter can be subjected to low-pass and high-pass filtering again to obtain the frequency band range desired to
Fig. 9 e (a) Grid current and, (b) the load current for PJD method in a frequency change condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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be examined. The detail and approximation coefficients of the 3-level wavelet transform applied and the amount of sample at each level are given in Fig. 6. After the signal is passed through the low-pass and highpass filters, the pair is sub-sampled and the resolution is halved in proportion to the number of samples. The frequency resolution doubles because the frequency band of the marking covers half of the previous frequency band. This process is repeated and the number of repetitions indicates the stream of WT.
The flowchart of the proposed method by WT is shown in Fig. 7. The proposed method uses the Daubechies wavelets of order 4 (db4) to detect the voltage interruption, voltage sag, voltage swell and transient disturbances in a HEBDG system.
Results The developed model is simulated under different grid conditions. This section presents obtained results from different
Fig. 10 e (a) Frequency, (b) grid voltage for PJD method in limited frequency change condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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operating conditions of developed fault detection methods for a HEBDG system.
Operating the HEBDG system with conventional fault detection methods Case 1: Voltage Swell Detection with Phase Jump Detection (PJD) Method: The proposed system is operated with PJD method. The frequency is stable at 50 Hz but the grid voltage increases from 220 V to 280 V. In this condition, the PJD method is capable of detecting the voltage swell. Fig. 8 shows the grid voltage and the load current. Case 2: Frequency Change Detection with PJD Method: The proposed system is also operated with PJD method in a frequency change. The grid voltage is stable at 220 V but the
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system frequency increases from 50 Hz to 51 Hz. In this condition, the PJD method is capable of detecting the fault and Fig. 9 shows the grid current and the load current. The load current is zero after the frequency change is detected. Case 3: Limited Frequency Change Detection with PJD Method: PJD method is also investigated in limited frequency changes. The grid voltage is stable at 150 V (Vpeak) but the system frequency increases from 60 Hz to 60.1 Hz and then decreases from 60 Hz to 59.95 Hz. In this condition, the PJD method is not capable of detecting the fault and Fig. 10 shows the frequency change and the grid voltage. PJD is not successful to detect the fault when the changes of defined threshold values are very limited. Case 4: Voltage Interruption Detection with Slip Mode Frequency Shift (SMS) Method: The developed system is also
Fig. 11 e (a) Grid voltage, (b) the load current for SMS method in a voltage interruption condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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operated with the SMS method in an active power change to detect the voltage interruption in a HEBDG system. The active power of load is adjusted to 0,9* Pinverter. In this condition, the SMS method is capable of detecting the voltage interruption event. Fig. 11 shows the grid voltage and the load current in a voltage interruption condition. Case 5: Islanding Detection with Slip Mode Frequency Shift (SMS) Method: The active power of the load is adjusted to 0,5* Pinverter. In this condition, the SMS method is also capable of detecting islanding. Fig. 12 shows the grid voltage and the load current and the load current is zero after islanding occurred. Also, islanding detection time is short compared to load power is adjusting to 0,5* Pinverter. These results show when the active power mismatch is high between the load and the grid,
detection time will be shorter. PJD method is not capable of detecting islanding in the non-detection zone (NDZ), thus the islanding detection is not possible in defined threshold values restricted by NDZ. The UVP/OVP & UFP/OFP methods have a large NDZ that covers all of the selected areas, and fault detection is not possible in this area. Also, PJD method has a smaller NDZ according to UVP/OVP & UFP/OFP methods. SMS method has the smallest NDZ comparing with other conventional fault detection methods. It is clear that SMS method is more reliable for fault detection compared to other conventional methods. Consequently, none of the inverter resident methods present a certain solution to detect reliably the faults in a HEBDG system.
Fig. 12 e (a) Grid voltage, (b) the load current for SMS method in an islanding condition. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Operating the HEBDG system with proposed WT-Based fault detection method The HEBDG system is operated with proposed WT-based PQ detection method. The frequency is stable at 50 Hz and the sampling frequency is selected as 10 kHz in the study. The proposed method uses the Daubechies wavelets of order 4 (db4) to detect the different PQ disturbances in a HEBDG system. Case 1: Voltage Sag Detection with Proposed WT-Based Method: In this condition, there is a voltage sag disturbance
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in the HEBDG system. The voltage sag condition that is shown in Fig. 13 starts at the 0.2nd second and finalize at the 0.4th second. The amplitude of the voltage signal is less than 30% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 13, and the voltage sag disturbance is clearly detected in the system thanks to the proposed method. Case 2: Voltage Swell Detection with Proposed WT-Based Method: The developed microgrid system is performed for a voltage swell condition. This condition that is shown in Fig. 14
Fig. 13 e The voltage sag detection by proposed WT method. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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is detected in the study, and it starts at the 0.15th second and finalizes at the 0.33rd second. The amplitude of the voltage signal is more than 20% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 14, and the voltage swell disturbance is detected in the system properly.
Case 3: Voltage Interruption Detection with Proposed WTBased Method: Voltage interruption detection is a significant issue for a HEBDG system. The developed microgrid system is performed for this condition by the developed WT-based method. The voltage interruption condition that is shown in Fig. 15 is detected in the study, and it starts at the 0.13th second and finalizes at the 0.33rd second. The amplitude of
Fig. 14 e The voltage swell detection by proposed WT method. Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Fig. 15 e The voltage interruption detection by proposed WT method.
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Fig. 16 e Detection transients in voltage signal by proposed WT method.
the voltage signal is the 1% of the nominal voltage between these seconds. The decomposition coefficients and the approximation coefficient of the WT are also shown in Fig. 15, and the voltage interruption disturbance is clearly detected in the system. Case 4: Detection Transients with Proposed WT-Based Method: The transients in a HEBDG system are also a significant issue. The transient condition that is shown in
Fig. 16 is detected in the study, and it starts at the 0.2nd second and finalizes at the 0.205th second. The amplitude of the voltage signal is the 180% of the nominal voltage and the frequency of the transient is 750 Hz between these seconds. The decomposition coefficients and the approximation coefficient of the WT are shown in Fig. 16, and the transient disturbance is immediately detected in the system.
Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183
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Conclusion [8]
A wavelet transform-based PQ disturbance detection method and the conventional fault detection methods are modeled for a HEBDG system and the performance assessment of these methods is presented in the paper. The implemented WT-based model is compared with fundamental inverter resident fault detection methods. The compatibility of these methods with the HEBDG systems could be performed easily thanks to the developed model. The phase jump detection method, over/under frequency method, over/under voltage method and slip mode frequency shift method are modeled and investigated for HEBDG systems. The generally used passive and active fault detection methods that are known as inverter resident methods are selected to assess the performance of these methods in the HEBDG systems. The proposed WT-based model has a flexible structure, so it is more suitable to implement with other conventional methods by integrating to the HEBDG systems. The proposed WT-based method uses both the frequency and the time domain analysis, thus the voltage changes in the grid signal could be detected easily according to the conventional fault detection methods. The transient detection is very difficult for conventional methods, but the WTbased method detects the limited frequency changes ontime. The results also verify that the proposed method is practical for the HEBDG systems to provide a safety connection to the grid.
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Appendix A. Supplementary data
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Supplementary data related to this article can be found at https://doi.org/10.1016/j.ijhydene.2018.06.183. [20]
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Please cite this article in press as: Bayrak G, Wavelet transform-based fault detection method for hydrogen energy-based distributed generators, International Journal of Hydrogen Energy (2018), https://doi.org/10.1016/j.ijhydene.2018.06.183