Determining an accurate fault location in electrical energy distribution networks in the presence of DGs using transient analysis

Journal Pre-proofs Determining an Accurate Fault Location in Electrical Energy Distribution Networks in the Presence of DGs using Transient Analysis Ehsan Gord, Rahman Dashti, Mojtaba Najafi, Athila Quaresma Santos, Hamid Reza Shaker PII: DOI: Reference:

S0263-2241(19)31134-0 https://doi.org/10.1016/j.measurement.2019.107270 MEASUR 107270

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Measurement

Received Date: Revised Date: Accepted Date:

19 July 2019 3 October 2019 13 November 2019

Please cite this article as: E. Gord, R. Dashti, M. Najafi, A.Q. Santos, H.R. Shaker, Determining an Accurate Fault Location in Electrical Energy Distribution Networks in the Presence of DGs using Transient Analysis, Measurement (2019), doi: https://doi.org/10.1016/j.measurement.2019.107270

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Determining an Accurate Fault Location in Electrical Energy Distribution Networks in the Presence of DGs using Transient Analysis Ehsan Gord 1, Rahman Dashti 2, Mojtaba Najafi 3, Athila Quaresma Santos4, and Hamid Reza Shaker 4 1- Department of electrical engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran Email: [email protected] 2- Electrical Engineering Department, Persian Gulf University, Bushehr 7516913817, Iran Email: [email protected] 3- Department of electrical engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran [email protected] 4- Center for Energy Informatics, University of Southern Denmark, Odense, Denmark; [email protected], [email protected] Abstract: Distributed generation resources are becoming more popular in electric energy distribution networks. As more Distributed generation are integrated into the grid, the system performance is challenged by issues such as manifold power injection to the network or nonlinear behavior when a fault occurs. To address this, fault location in electric energy distribution networks in the presence of distributed generation needs particular attention. This is important to reduce the loss of generated energy, reduce interruptions time, increase the reliability of the network and consequently improve the security of electricity supply. In this paper, a novel fault location method is presented applied to distributed networks with distributed generation. The proposed method is a hybrid two-step method which identifies accurate fault location using information stored in the network at pre- and post-fault time. The proposed method employs voltage and current information at the beginning of the feeder to estimate fault distance in the first step. The estimated distance will be associated with several similar sections considering the topology of the distributed networks. In the second step, the proposed method determines accurate fault location through transient analysis based on the frequency component. In this step, the exact fault location is identified. In order to investigate its performance, a standard IEEE-11 network is simulated in MATLAB. Furthermore, experiments are carried out in a network power simulator, showing good results. Keywords: Distribution System, Fault Location, Transient Analysis, Distributed Generation

1. Introduction To improve the security of the electricity supply, there is an increasing demand for improving the protection indices of distribution systems. The presence of Distributed Generation (DG) in distribution systems introduces more challenges to protection algorithms. To give an example, when a fault occurs in distribution networks consisting of DGs, a short circuit current is injected to the network. Accurate fault location and identification in these networks is very important to solve outages and to restore the system’s services. To address these challenges, over the last few years, different fault location schemes have been proposed. Some of fault location schemes are based on modifying or resynchronizing the existing protection devices of the distribution system in the presence of DGs. When a fault occurs, these methods try to use information from protection devices to detect the faulty section. These methods identify the faulty section based on the device which has interrupted the circuit e. g. [1], [2]. However, due to the extent of electric energy distribution networks (DNs) and their requirement to different devices, such solutions are neither efficient nor economic. There are other methods in the literature which use neural networks to determine fault location e. g. [3], [4]. These methods consider DGs in a neural network trained by system information [3] and [4]. Taking into account the continuous changes in distribution systems, such methods reduce the accuracy of the fault location. Some other methods have also been proposed to locate faults in distribution systems in the presence of DGs, which use the symmetric component [5]-[9]. In these methods, an equation is presented for different faults to determine the possible fault locations.

Nomenclature Vsa: voltage of phase a at the beginning of the feeder x: distance of fault point from beginning of the feeder Zlaa: impedance matric per unit length If: current at fault point Rf: fault resistance ρ: location index 𝑣𝑎𝑓 : result of frequency transform used in the proposed method 𝑣𝑎 : voltage of the phase in which fault has occurred (adopted from relay at the beginning of the feeder) t1: time at which fault starts t2: time at which transient data fault reach to steady state (approximately, 1/3 cycle). ε𝑠 : Difference of the index obtained using the proposed method between possible fault location 𝑛 and real fault location GPS: Global Positioning System Z1 to Zn: Impedance matrix of nodes, 1 to n: Number of nodes S: Source: beginning of the feeder, 𝑉𝑠𝑎: voltage at the beginning of the feeder 𝐼𝑠𝑎: Current at the beginning of the feeder, L-x: Difference between L and x x: distance of fault location from beginning of the feeder L: total length of the distribution line, RF: resistance of the fault point 𝐼𝐹𝑎 : Current of the fault point, 𝑉𝐹𝑎 : voltage of the fault point Zr: load impedance, 𝐼𝐿𝑎 : load current K: a symbol used to determine real numbers from 1 to n Yk: admittance of node k, Zk: impedance of node k, Vk: voltage of node k Vk+1: voltage of node k+1, 𝐼𝐿𝑘 : load current at node k, Ik-1: node current before node k Ik: current of node k L: total length of the section, x: distance of the beginning of the feeder from fault point Vs and Is: voltage and current of the beginning of the section, i: current of the faulty section 𝑌 x: half of admittance of the line at a distance of x, Zx: impedance of the line at a distance of x 2 Iu: output current of the fault point, ID: current entering the fault point, F: fault point VF: voltage of the fault point, VR and IR: voltage and current of the end of the section 𝑌 (L-x): half of admittance of the line at a distance of L-x, 𝑍(𝐿−𝑥) : impedance of the line at a 2 distance of L-x The type of DG should also be specified for these methods. The shortcoming of these approaches is that they employ a short line model which is not good for the transient mode. In [10], an intelligent fault location method is proposed. In this method, possible fault locations are first determined using the impedance method, then real fault location is detected based on proportional voltage created at fault point and possible fault locations. The method uses the 𝜋 line model and the capacitance of the line while the evaluation and testing are neglected. Ignoring the capacitance of distribution lines and considering its significant impact on fault location method, can affect the final results. Lastly, as fault resistance is 25 ohms the method is sensitive to fault resistance. All studied references require specific features and characteristics to be employed in distribution systems because they do not consider general conditions of a distribution system. In [11], a fault location method in DNs is studied. As mentioned before, the process of successful fault location using the impedance method in DNs, consists of two tasks: 1) estimating the fault distance and 2) determining real fault location among locations match the estimated distance. In the method presented in [11], only fault distance estimation is considered and a sample network with 11 nodes and 𝜋 line mode are used. In [12], a 13-node sample network with DG resources, according to the IEEE standard, has been considered for fault location. In [12], the network

is first divided into two sections: 1) faults before the section which has DGs and 2) faults after section which has DGs. Then, two separate methods are considered where each one uses a separate formula to determine fault location. The considered line model in simulations is a lumped line model. In [13], modern networks have been considered for fault location and the intelligent methods are used. The intelligent method used in [13], requires information about voltage, current and frequency of the network in order to locate faults. This method is very costly, and it cannot be applied to all networks. Final users in distribution networks are loads in the distribution system; smart meters are used to measure their electric energy consumption, automatically. Such systems require advanced communication channels for data exchange which is provided via communication data transmission lines. These sections are put together to improve fault location in distribution networks; but, specific condition of distribution networks increases the complexity of this issue [14]. There are some methods which monitor the network accurately to locate faults. For this, a large number of specific devices mentioned in [15] are required. All information collected by these devices are given to the operator to be analyzed and a GPS (Global Positioning System) is used to introduce fault location [15]. A specific parameter that should be considered is the mutual couplings in electric power networks, because in the most cases, that can result in a significant increase in the total impedance of the line [16]. Changes in characteristics and impedance of power lines, especially distribution networks, deviate network parameters from real values changing relay settings at the beginning of the feeder. The performance of the protection zones is affected and results in significant failure in protection of the distance feeder algorithm [17]. In distribution networks which are the last section of power systems, considering some issues is of great importance. Usually, information is only available at the beginning of the feeder; this information is extracted from the feeder at the beginning of the relay. Considering the main and side branches, the relay cannot be used in all branches; this is not feasible and cost-effective in practice [18]. From the same category of the fault location methods, we can mention the negative sequence method. It models characteristics and parameters of conductors of network lines. The simulation results show that the implementation of the algorithm does not offer a good measure of parameters of real distribution lines [19]. Methods based on traveling waves are another class of methods used for fault location in distribution networks. It operates based on frequency and wave propagation speed of the fault wave. The detection of the faulty section is based on information of reciprocating waves from fault point and its analysis [20]. Dead-band range is another measurement considered for fault location in DNs. When the input is changed, it takes some time to notice the effect of the change at the output and analyze the resulted condition. The method presented in [21] has employed this fact for fault location and introduced a new path for making the network reversible by means of determining undistributed energy. Similar to the previous method, other algorithms have been presented, that employ specific conditions caused by fault for distance estimation. In some cases, these methods use central control points; some points of the network are designed to record the required information and to use it to determine fault location [22]. In [23], fault location in electric energy distribution network is investigated considering the distributed line model of distribution networks. In this method, in order to locate faults, fault distance is determined first and then considering fault distance, the main fault point is determined. It should be said that the absence of distributed generation resources and considering their impact on testing can result is a shortcoming. This is because the distributed generation resources are, nowadays, widely used in the distribution networks and cannot be ignored. In [24], transient state analysis is used for fault location employing mathematical morphology function. This function is used to extract information from high-frequency signals and is less affected by noise. The method in [24] operates based on information of two terminals, which is a shortcoming for distribution networks. In distribution networks, information only exists in the relay at the beginning of the feeder because distribution networks have scattered side branches and sections. In [25], a method has been presented for protection of the network against single-phase to ground fault. This method is designed based on traveling waves

which detects fault location using polarity of the first received traveling wave when a fault occurs. The corresponding relay operates based on the fault location being upstream or downstream. This is only done to protect the network (like distance relay). However, the problem which is addressed in our paper is fault location in electric distribution networks, therefore the focus is on location of an occurred fault as a key step toward service restoration and recovery. Furthermore, the method in [25] can only resolve single-phase to ground faults. In [26] and [27], methods are presented based on transient state analysis of the traveling waves. In [26], the method is mainly based on directional protection which is done based on fast Fourier transform to extract information through traveling wave analysis. To this end, [27] has presented penetration factor of DG resources in the protection of electric distribution networks which reduces accuracy and efficiency of the protection methods. The method tries to use traveling waves method to locate fault in these networks. The main problem of these methods is the specific topology of the power distribution networks. Because these networks have a lot of feeders and subfeeders in their topology, when travelling waves issue is presented, input transient states are attenuated as a result of which, receiving high quality travelling waves would be difficult and costly. In addition, various discontinuities along the travelling waves’ path, sudden changes of current and voltage make these methods inefficient in real conditions. In this paper, a hybrid fault location is proposed in different multiple branch distribution systems. This technique employs information stored in relays installed at the beginning of the feeder. After determining possible fault locations, voltage frequency component analysis is used to determine the real fault location. This analysis is performed on transient of the faulty voltage waveform. The rest of this paper is organized as follows: Section 2 presents the impedance method used to determine fault distance, its algorithm and formulations, then the transient state analysis method for detecting real fault location is presented. The results of simulating the feeder and data analysis in MATLAB are presented in Section 3. Finally, conclusions are presented in Section 4. 2. The Proposed Method In the following, we describe the proposed method in detail. However, the method is also summarized as flow-chart later in the paper (Figure 9) to give a better general overview. The proposed method collects pre and post-fault data from voltage and current signals at the beginning of the feeder or DG source. It also assumes that information is adopted using Global Positioning System (GPS). The load on each node is calculated based on the method proposed by [12]. In addition, pre-fault current values are used to define the matrix of node impedance for the distributed feeder, where each section is modeled using a distributed line model. 2.1. The Proposed Method for Calculating Fault Distance in the Distribution Network in the Presence of DGs The proposed method is based on a generic distributed network system with DGs, such as the one described in Figure (1).

Figure (1). Distribution system in the presence of DGs

The variables used in Figure 1 are: Z1 to Zn: Impedance matrix of nodes, 1 to n: Number of nodes The generator connected to bus K is connected to nodes 1 to k-1 from the left side and nodes k+1 to n from the right side. The fault location algorithm is proposed considering DGs. The algorithm is initialized considering fault at the beginning of the feeder, before the source. In fact, the proposed method starts the calculations using the information stored at the beginning of the feeder (voltage and current information are stored at DG source node, also). Before discussing the proposed algorithm, a brief description of calculating fault location in the system in the absence of DGs is presented. 2.1.1. Fault Location in the Absence of DGs To give a better understanding, a circuit is designed as shown in Figure (2). It can be seen that there are loads after fault point which are called as Zr and the current passing through these loads is called ILa. Eq. (1) is used to obtain the voltage value at the local terminal (more information can be found in [24]). 𝑣𝑆𝑎 = 𝑥 ∗ (

𝑦𝑧

𝑙 2 𝑎𝑎

∗ 𝑖𝑎 +

𝑦𝑧

𝑙 2 𝑎𝑏

∗ 𝑖𝑏 +

𝑦𝑧

𝑙 2 𝑎𝑐

∗ 𝑖𝑐 ) + 𝑖𝑓 𝑅𝑓

(1)

Where: Vsa: voltage of phase a at the beginning of the feeder x: distance of fault point from the beginning of the feeder Zlaa: impedance matrix per unit length

Figure (2). Faulty distribution system

The variables used in Figure 2 are: S: Source: the beginning of the feeder, 𝑉𝑠𝑎: voltage at the beginning of the feeder 𝐼𝑠𝑎: Current at the beginning of the feeder, L-x: Difference between L and x x: distance of fault location from the beginning of the feeder L: total length of the distribution line, RF: resistance of the fault point 𝐼𝐹𝑎 : Current of the fault point, 𝑉𝐹𝑎 : voltage of the fault point Zr: load impedance, 𝐼𝐿𝑎 : load current If the fault is on a single-phase line (phase a), the voltage is obtained by Eq. (2). 𝑣𝑆𝑎 = 𝑥 ∗ (

𝑦𝑧

𝑙 2 𝑎𝑎

∗ 𝑖𝑎 ) + 𝑖𝑓 ∗ 𝑅𝑓

There are three unknowns in Eq. (2): x: distance from the fault point to the beginning of the feeder If: current at fault point Rf: fault resistance

(2)

It should be mentioned that, equations are presented in steady sinusoidal state, therefore by expanding the equations and calculating real and imaginary parts of Eq. (2) and eliminating fault resistance, the fault distance can be calculated by Eq. (3): 𝑣𝑎 𝑟 ∗𝑖𝑓 −𝑣𝑎 𝑖 ∗𝑖𝑓 𝑖 𝑟 𝐴∗𝑖𝑓 −𝐵∗𝑖𝑓

𝑥=

𝑖

(3)

𝑟

Where the values of A and B are defined by Eq. (4) and (5), respectively. 𝑍𝑦 𝑙𝑎𝑎𝑟 2

𝐴= 𝐵=

𝑍𝑦 𝑙 2 𝑎𝑎𝑟

∗ 𝑖𝑎𝑟 −

∗ 𝑖𝑎𝑖 +

𝑍𝑦 𝑙 2 𝑎𝑎𝑖

𝑍𝑦 𝑙 2 𝑎𝑎𝑖

∗ 𝑖𝑎𝑖 +

∗ 𝑖𝑎𝑟 +

𝑍𝑦 𝑙 2 𝑎𝑏𝑟

𝑍𝑦 𝑙 2 𝑎𝑏𝑟

∗ 𝑖𝑏𝑟 −

∗ 𝑖𝑏𝑖 +

𝑍𝑦 𝑙 2 𝑎𝑏𝑖

𝑍𝑦 𝑙 2 𝑎𝑏𝑖

∗ 𝑖𝑏𝑖 +

∗ 𝑖𝑏𝑟 +

𝑍𝑦 𝑙 2 𝑎𝑐𝑟

𝑍𝑦 𝑙 2 𝑎𝑐𝑟

∗ 𝑖𝑐𝑟 −

∗ 𝑖𝑐𝑖 +

𝑍𝑦 𝑙 2 𝑎𝑐𝑖

𝑍𝑦 𝑙 2 𝑎𝑐𝑖

∗ 𝑖𝑐 𝑖

∗ 𝑖𝑐𝑟

(4) (5)

The fault resistance is also calculated by Eq. (6). 𝑅𝑓 =

−𝐵∗𝑣𝑎 𝑟 +𝐴∗𝑣𝑎 𝑖 𝐴∗𝑖𝑓 −𝐵∗𝑖𝑓 𝑎𝑖

(6)

𝑎𝑟

In these equations, subscript (r,i) indicates the real and imaginary parts and if is the fault current calculated based on the relationship between the load current and the line current. 𝑖𝑓 = 𝑖𝑙𝑎 − 𝑖𝑎 (7) Using the same method used to calculate phase-to-ground fault, equations for other fault states can be obtained, such as phase to phase and three-phase faults. Finally, the following algorithm is used to estimate fault distance: a) ila is assumed to be the main load current before the fault. b) Eq. (7) is used to estimate the fault current. c) Eq. (3) estimates the fault location. (In this step, two conditions are considered for the calculated fault distance (x): 1) The obtained distance should be real and 2) The calculated distance should be less than the line length. If these conditions are not met, the algorithm should be repeated again.) d) The estimated voltage is calculated using the following equation: 𝑣𝑓 𝑣𝑠 𝑎 𝑎 [𝑣𝑠 𝑏 ] = [𝑣𝑓 𝑏 ] − 𝑥 ∗ 𝑣𝑠 𝑐 𝑣𝑓 𝑐

𝑧𝑦

𝑙 2 𝑎 𝑧𝑦 𝑙 2 𝑏 𝑧𝑦 [ 2 𝑙𝑐

∗ 𝑖𝑠 𝑎 ∗ 𝑖𝑠 𝑏

(8)

∗ 𝑖𝑠 𝑐 ]

e) A new load current is calculated using the fault voltage vf calculated in the previous step. f) Using the obtained load current, the algorithm is repeated again from step (b). To obtain the circuit current, Kirchhoff’s voltage and current laws are used. For this purpose, information of voltage, load and network impedance are used. Relations given in [12] are used in calculations and proofs. These calculations converge to one value which is used as the fault point in the distribution network. In order to illustrate this step, the analysis is described by Figure (3), where voltage and current values are calculated by Eq. (9), (10) and (11). This procedure reduces errors associate with line losses and from the topology of unbalanced distribution networks.

Figure (3). Updated voltage and current at the local terminal

The variables used in Figure (3) are: K: a symbol used to determine real numbers from 1 to n Yk: admittance of node k, Zk: impedance of node k, Vk: voltage of node k Vk+1: voltage of node k+1, 𝐼𝐿𝑘 : load current at node k, Ik-1: node current before node k Ik: current of node k 𝑣𝑘+1 = 𝑣𝑘 − 𝑧𝑘 ∗ 𝑖𝑘

(9)

𝑖𝑙𝑘 = 𝑣𝑘 ∗ 𝑦𝑙𝑘

(10)

𝑖𝑘 = 𝑖𝑘−1 − 𝑖𝑙𝑘

(11)

As voltage and current are updated, the algorithm starts again and indicates a new location of the fault more accurately. This process is continued until convergence is reached at the estimated fault location 2.1.2. The Proposed Fault Location Algorithm in the Presence of DGs The algorithm proposed in Section 2.1 is extended to consider DG: a) ila is assumed to be the load current before the fault location. b) Eq. (7) is used to calculate fault current. c) Eq. (3) is used to estimate fault distance. d) Voltage of the fault point is calculated using Eq. (8) (the system comprising DGs described in [12] is used for this purpose). Voltage and current of the station, before fault location are calculated. Considering Figure (3), the voltage of the fault point is calculated by Eq. (12). 𝑣𝑓 = 𝑣𝑛−1 − 𝑖𝑛−1 ∗ 𝑧𝑙𝑖𝑛𝑒 ∗ 𝑥 (12) Where Zline is the line impedance per kilometer of the faulty section and x is the distance from the station n-1 to the fault point. e) Using the voltage obtained at the fault point in the previous step, Thevenin's equivalent circuit of the system is obtained by e.1) and e.2). e.1) If estimated fault location at step (3) is after the DG, the equivalent circuit becomes parallel with the loads integrated with line impedance after the fault point. At this time, the current is updated again (indeed, in this case, voltage and current are updated using information of the first station before fault point). This current is obtained using the following equation. −1 𝑖𝑙 𝑎 = 𝑣𝑓 𝑧𝑡ℎ (13) e.2) In this step, it is considered that fault has occurred in a point before the section including DGs and equivalent Thevenin's equivalent circuit designed for this purpose includes the DGs. The following formula is used to calculate the required current: −1 𝑖𝑙 𝑎 = 𝑧𝑡ℎ (𝑣𝑓 − 𝑣𝑡ℎ ) (14) f) The algorithm returns to step (b). Similar to the techniques described for distribution systems without DGs. The output of the algorithm is the fault distance from the beginning of the feeder. As specified, the purpose of using step calculations and updating voltage and current values in an iteration loop is to improve quality and accuracy of calculations in estimation of the fault location. This is continued until conditions defined for fault point are met so that the algorithm converges to one point.

2.1.3. DG Model

The electrical model of the DG used in this paper is a synchronous generator model shown in Figure (4).

Figure (4). Model of the Distribution Generator

This model is a combination of 𝑋 ′′ 𝑠 (transient reactance of the machine), 𝒓𝒂 (armature resistance) and 𝐸′′𝑔 (internal voltage) for the synchronous generator. 2.2 Distributed Line Model and Expanding the Equations The line model used in this paper is the distributed line model shown in Figure (5) [23]. is

zx

i

i

Vf

iD

+

z(l-x) iR

iu

+

f

VR

V

s

Y 2

Y 2

x

x

Y (L-x) 2

Y (L-x) 2

-

-

L

L-x

x Figure (5). Distributed line model

The variables used in Figure 5 are: L: total length of the section, x: distance of the beginning of the feeder from fault point Vs and Is: voltage and current of the beginning of the section, i: current of the faulty section 𝑌 2

x: half of admittance of the line at a distance of x, Zx: impedance of the line at a distance of x

Iu: output current of the fault point, ID: current entering the fault point, F: fault point VF: voltage of the fault point, VR and IR: voltage and current of the end of the section 𝑌 2

(L-x): half of admittance of the line at a distance of L-x, 𝑍(𝐿−𝑥) : impedance of the line at a distance of

L-x Point f is the fault location point. It divides the line into two sections i. e. x and L-x. Proof of the proposed method is continued through analyzing relationships from the beginning of the loop: −𝑣𝑠 + (𝑖𝐷 +

𝑦𝑥 𝑣 ) (𝑧𝑥) + 𝑣𝑓 2 𝑓

Where: 𝑦𝑥 𝑣𝑠 = 2 𝑣𝑓 𝑧𝑥 + 𝑧𝑥 𝑖𝐷 + 𝑣𝑓

=0

(15)

(16)

Therefore: 𝑣𝑠 = (1 +

𝑦𝑥 2 𝑧) 𝑣𝑓 2

+ 𝑧𝑥𝑖𝐷

(17)

And 𝑦𝑥 𝑖𝑠 = 2 𝑣𝑠 + 𝑖

(18)

Where: 𝑖𝑠 =

𝑦𝑥 𝑦𝑧 [(1 + 2 𝑥 2 ) 𝑣𝑓 2

+ 𝑧𝑥𝑖𝐷 ] + [𝑖𝐷 +

𝑦𝑥 𝑣 ] 2 𝑓

(19)

Thus: 𝑖𝑠 =

𝑦𝑥 𝑦𝑧 𝑦𝑧 [(1 + 2 𝑥 2 ) 𝑣𝑓 ] + ( 2 𝑥 2 𝑖𝐷 ) + 𝑖𝐷 2

+

𝑦𝑥 𝑣 2 𝑓

(20)

Therefore: 𝑖𝑠 =

𝑦𝑥 (2 2

+

𝑦𝑧 2 𝑥 ) 𝑣𝑓 2

+ (1 +

𝑦𝑧 2 𝑥 ) 𝑖𝐷 2

(21)

Therefore, it can be written: 𝑣𝑠 𝐴 𝐵𝑥 𝑣𝑓 [𝑖 ] = [ 𝑥 ][ ] 𝑖𝐷 𝐶 𝐷 𝑠 𝑥 𝑥

(22)

Parameters Ax, Bx, Cx and Dx are coefficients of Vf and ID in equations (17) and (21), respectively, as represented in Eq. (22). The above equations are given for loop x, and it is calculated similarly for loop (l-x): −𝑣𝑓 + (𝑖𝑅 +

𝑦(𝑙−𝑥) 𝑣𝑅 ) (𝑧(𝑙 2

− 𝑥)) + 𝑣𝑅 = 0

(23)

Where: 𝑦𝑧 𝑣𝑓 = 𝑖𝑅 (𝑧(𝑙 − 𝑥)) + (𝑙 − 𝑥)2 𝑣𝑅 + 𝑣𝑅

(24)

2

Therefore: 𝑣𝑓 = (1 +

𝑦𝑧 (𝑙 2

− 𝑥)2 ) 𝑣𝑅 + (𝑧(𝑙 − 𝑥))𝑖𝑅

(25)

And 𝑦

𝑦

𝑖𝑢 = (2 (𝑙 − 𝑥)) 𝑣𝑓 + (𝑖𝑅 + 2 (𝑙 − 𝑥)𝑣𝑅 )

(26)

Thus: 𝑦

𝑖𝑢 = 2 (𝑙 − 𝑥)[(1 +

𝑦𝑧 (𝑙 2

𝑦

− 𝑥)2 ) 𝑣𝑅 + (𝑧(𝑙 − 𝑥))𝑖𝑅 ] + 𝑖𝑅 + 2 (𝑙 − 𝑥)𝑣𝑅

(27)

Therefore: 𝑖𝑢 = (2 +

𝑦𝑧 (𝑙 2

𝑦

− 𝑥)2 ) 2 (𝑙 − 𝑥)𝑣𝑅 + (1 +

𝑦𝑧 (𝑙 2

− 𝑥)2 ) 𝑖𝑅

Therefore, it can be written: 𝑣𝑓 𝐴(𝑙−𝑥) 𝐵(𝑙−𝑥) 𝑣𝑅 [ ]=[ ][ ] 𝑖𝑢 𝐶(𝑙−𝑥) 𝐷(𝑙−𝑥) 𝑖𝑅

(28)

(29)

Eq. (29) is similar to Eq. (22) in which parameters are coefficients of V f and ID in equations (25) and (28). Finally, equations obtained using the proposed method in previous steps are calculated. The value obtained for x determines several possible fault locations. Detecting the main fault location among possible locations using the proposed method is presented in the following section. 2.3. The Proposed Method for Determining Real Fault Location

In this step, the proposed method specifies the main fault location among possible fault locations. This method by introducing indices for each possible fault location, based on frequency of the fault location, identifies the section with the minimum value of indices as the main fault location. The algorithm designed for this method is presented in the following: 1. Determining an index using the frequency component of the real fault voltage 2. Creating similar fault at other possible locations (using the impedance method) 3. Determining an index for all possible fault locations 4. Determining the minimum index among all locations based on real fault index and simulation fault at all locations 5. Determining the main fault location based on the minimum index value 2.3.1. Determining Index The proposed technique uses the frequency component analysis of the real fault voltage. This needs to be compared it with its frequency counterparts obtained from the simulated faults at possible locations. The process is a three-step process: Step 1) Simulation of fault at the possible fault locations which are obtained from the algorithm described in Section 2.1.2. and obtaining the voltages. Step 2) The absolute value of frequency Fourier transform of the fault voltage waveform which is defined as location index (i. e. ρ in this paper) is calculated for the real fault and for fault at possible locations. Step 3) A quantitative measure (i. e. ε in this paper) is calculated which identifies which possible location has the closest index to the real fault index. As mentioned in the proposed algorithm, a characteristic coefficient is calculated for each section using the frequency component of the fault voltage. In this section, the voltage waveform of one of the phases (Phase a), when no fault has occurred, is shown in Figure (6).

Figure (6). The voltage waveform of Phase a in normal state

Figure (7) shows the voltage of Phase a when a fault has occurred.

Figure (7). The voltage waveform of Phase a when a fault has occurred.

The transient created at voltage waveform of Phase a can be seen in Figure (7). The fault might occur in any of the main or side branches of the distribution system. The voltage stored at the beginning of the feeder is a sinusoidal voltage. Now the proposed method calculates the index using the following steps and calculations: First, it should be mentioned that the proposed method employs the Fourier transform at this step. As some functions can be written as Taylor expansion of polynomial functions, periodic functions can be written in terms of sinusoidal functions with arbitrary initial phase and coefficient. The signal is transformed using the Fast Fourier Transform (FFT). Using Discrete Fourier Transform, discrete signals and functions can be transformed from time domain to frequency domain [28],[29]. At this step, the frequency component of the network voltage is determined as follows: 𝑣𝑎𝑓 = 𝐹𝐹𝑇(𝑣𝑎 ) (30) The details and formulations used in this section are described and edited in Appendix A. Where: ρ = 𝑣𝑎𝑓

(31)

At this step, we proceed by defining the index which is represented as ρ: In fact, ρ is an index for each possible location extracted from the transient frequency component of the voltage. This transient is caused by fault. Its formula is extracted as follows: ρ = 𝑣𝑎𝑓 = |𝐹𝐹𝑇(𝑣𝑎 (𝑡1 : 𝑡2 )| (32) ρ: Location index (the result of the frequency transform used in the proposed method) 𝑣𝑎 : Voltage of the phase in which the fault has occurred (adopted from the relay at the beginning of the feeder) t1: time at which fault starts t2: time at which transient data fault reach to steady state (approximately, 1/3 cycle). ρ𝑚 : Extracted characteristic coefficient of the real fault (using fault voltage stored at the relay at the beginning of the feeder) ρ𝑙 : Index extracted for possible locations (using simulation and theoretic calculations)

In order to describe Eq. (32), its diagram is shown in Figure (8). In this section, it is necessary to describe that t1 and t2 are starting and ending points of this diagram in the time domain before transformation to frequency domain.

Figure (8). The voltage waveform of the faulty phase based on index (Diagram of (ρ = 𝑣𝑎𝑓 ))

The proposed method and decision-making algorithm are used to determine the minimum index. Thus, variations of ρ𝑚 with respect to ρ𝑙 is specified for each location. These variations are the performance basis of the proposed method which is called ε hereafter. In this step, fault location based on minimum value of ε is determined. If a fault occurs in one distribution feeder and the proposed location method is applied, then “n” possible fault locations would be detected, and one index is calculated for each location which results in Eq. 33: ε 𝑠 = ρ𝑚 − ρ𝑙 (33) 𝑛

𝑛

1:𝑛

Where: ε𝑠 : The difference of the index, obtained using the proposed method, between possible fault location 𝑛

and real fault location ρ𝑚 : Index extracted for real fault occurred in the sample system. 𝑛

ρ𝑙 : Index extracted for all possible fault locations of the feeder (first to n th branch using simulation 1:𝑛

and theoretic calculations) In the following, a general schematic and procedure to achieve the accurate fault location in electric energy DNs is designed by the flowchart shown in Figure (9).

First Step of the Proposed Method

Start (Detecting Fault and Its Type)

(1)

Receiving Voltage and current Information at the beginning of the Feeder and Nodes with DG

(2)

Calculating voltage, current and equivalent load of each section using the method presented in [12] (3) Load current (IL) is assumed to be the same before and after fault

Calculating input current assuming that system current and post-fault currents are the same

(9) Updating load and network currents using Eqs.(9)-(11)

(4)

Calculating fault current using Eq.(7)

(5)

Calculating fault distance (x) using Eq.(3)

(6)

Is the calculated fault distance (x) accepted? (has it converged)

(7)

(8) Determining voltage and current again using Eq.(8)

No

Yes

Second Step of the Proposed Method

Determining sections with represent fault distance in the network covered by innovative method

Theoretic calculations of similar faults introduced in each section of step (10)

(10)

(11)

Extracting characteristic for each section of step (11), Eq.(32)

(12)

Extracting characteristic for the real fault stored at the beginning of the feeder

(13)

Calculating Index of the proposed method using Eq.(33)

(14)

Determining smallest Index of the proposed method

(15)

Determining the section correspondent to the Index selected in step (15)

(16)

Introducing the faulty section along with fault distance (x)

(17)

End

(18)

Figure (9). Flowchart of the proposed method for determining accurate fault location in electric power DN

The flowchart is divided into two general parts, as mentioned in the proposed method. The first part is dedicated to distance measurement, where the output is the fault distance. In the second section, the measured distance is used to introduce sections of the network in which fault might have occurred. In the end, the proposed method introduces the location with the minimum value of ε as the real fault location. 3. Performance Evaluation of the Proposed Method 3.1. Sample Network In order to evaluate performance of the proposed scheme, an 11-node feeder, shown in Figure (10), is simulated using MATLAB. The sample 11-node system is one with distributed line model with a DG on node 11. Sample system is simulated based on standard IEEE-11 network.

m

Node 9

1.7 k

Node 1

Node2 Node6

Node 4 0.7 km

Node3

2.7 km

Node7 2.6 km

0.7

1.6 km

7.763 km

km

5 km

0.763 km

Feeder

DG

3.2 k

m

Node 11

Node 5 Node 10

Node8

Figure (10). Single-line diagram of the sample 11-node feeder

In addition, each node has an equivalent load under the number of the same node. In each simulation case, voltage waveform before and during the fault is measured and stored. The DFT is used to analyze voltage frequency component. Characteristics of the studied network are shown in Table (1). Table (1). Characteristics of the studied 11-node network Voltage source PV voltage X/R Short circuit power Load model Total line length Wire type

20KV 20KV 4 100MVA Impedance 3 phase 29.596 ABSR-Dog

In order to simulate the sample network, distributed line model is used at a frequency of 50Hz with a sampling rate of 25KHz; that is, 500 samples are taken for each cycle (one sample is recorded for each 40*10-6s). These are tested for both networks. 3.2. Analyzing Simulation Results 3.2.1 Analyzing Results for Determining Fault distance The accuracy of the proposed method in determining fault distance considering different fault resistances, distances and starting angle is investigated in this section. 1) Fault Resistance: The proposed method is tested under constant changes of fault resistance. Simulation is performed for fault resistances varying from 0 to 100 ohm in the sample network and the results are stored. A fault with a resistance of 100 ohms is considered for analyzing the accuracy of the

proposed method in high impedance faults, as this is one of the key shortcomings of fault location methods in DNs. 2) Fault Distance: The performance of the proposed method is analyzed for different distances. Faults in different sections of the distribution systems are simulated and the results are stored. In the following, results along with descriptions are considered. Knowing information about the three phases at the beginning of the feeder and measuring voltage and current of theses phases, deviation of distance measurement can be obtained using Eq.(34). 𝑒𝑟𝑟𝑜𝑟 =

𝑥𝑎𝑐𝑡𝑢𝑎𝑙 −𝑥𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑙𝑡

(34)

xactual : real fault distance xcalculated : calculated fault distance

l t : total feeder length a) Effect of different fault resistances along with fault type for distance estimation: Singlephase to ground, two-phase to ground and three-phase to ground faults with resistances of 0, 50 and 100 ohm are used to simulate and calculate fault percentage of the algorithm; the results are given in Table (2). Starting angle for all faults is considered to be 45 degrees. Results which are given in Table (2) show that the maximum and the minimum error of the proposed method are 0.5% and 0.005%, respectively. The results indicate high accuracy of the proposed method and, therefore the effect of fault type on accuracy of the algorithm is negligible. Here, it is necessary to present more descriptions on the effect of fault resistance on distance estimation in the studied system, comprising distributed line model. As mentioned in Section 2, a hybrid method is used to estimate fault distance. Considering the presented results in Table (2), good performance of the proposed method is obvious.

b) Effect of fault distance and variations of fault angle on the fault distance estimation algorithm: In order to analyze the starting angle, different faults with the following conditions are applied:  Different fault types (single-phase to ground, two-phase to ground and three-phase to ground)  Different starting angles (0°, 45°, 90°, 130°, 170°)  Fault resistance of 0 ohm In addition, the fault is changed continuously by applying fault to different branches. Table (3) shows the error percentage of the algorithm in a number of branches of the studied network which is simulated and calculated in MATLAB. Obtained results show that maximum error of the algorithm is 0.58%, for three-phase fault with starting angle of 170°, and its minimum is 0.0001, for single-phase fault with starting angle of 170°. The results indicate high accuracy of the proposed algorithm. Table (2). Effect of different fault resistances on the proposed algorithm for distance estimation Fault location

Fault type Threephase to ground

Two-phase to ground

Fault resistance

Singlephase to ground

Error percentage

0.1018

0.0125

0.0057

1-2

0.2049

0.0886

0.0112

2-3

0

0.1119

0.0141

0.0748

3-9

0.5340

0.1985

0.1356

5-11

0.3995

0.1201

0.0152

1-2

0.3600

0.0707

0.0338

2-3

0.1375

0.0047

0.0573

3-9

0.2680

0.1786

0.2188

5-11

0.0673

0.1248

0.0081

1-2

0.2232

0.0714

0.0193

2-3

0.1723

0.0047

0.0115

3-9

0.2680

0.1796

0.2141

5-11

50

100

Table (3). Results of the Proposed distance estimation algorithm along with distance variations and different types of fault with different angles Fault starting angle 170

130

90

45

0

Fault location

0.0085 0.0085 0.0808 0.0741 0.0355 0.0619 0.0194 0.0896 0.0264 0.0050 0. 0478 0.1383

1-2 2-3 3-9 5-11 1-2 2-3 3-9 5-11 1-2 2-3 3-9 5-11

Error percentage

0.0010 0.0101 0.1911 0.1354 0.0152 0.0169 0.1163 0.1312 0.0463 0.1167 0.2889 0.5804

0.0057 0.0122 0.1911 0.1650 0.0563 0.0358 0.0078 0.1985 0.0690 0.0707 0.3064 0.4576

0.0105 0.0207 0.0829 0.0548 0.0520 0.0145 0.1782 0.2148 0.0911 0.1162 0.1458 0.2513

0.0057 0.0112 00748 0.1356 0.0125 0.0886 0.0141 0.1985 0.1018 0.1049 0.3560 0.5492

Fault resistance Single phase to ground Two phase to ground

Three phase to ground

In conclusion, the analysis of the results obtained from simulation for the proposed distance estimation method, confirms that the results are satisfactory and the goal for determining possible fault locations under different conditions is achieved. In the following, simulation results for analyzing the proposed method in determining real fault location are presented.

3.2.2. Analysis of the Results for Determining Real Fault Section: In this section, the proposed method selects the real fault location among all possible fault locations. The proposed algorithm determines the index presented in Section 2.2.1 to determine the main fault location. Table (4) presents results obtained by applying the proposed method to determine the main fault section using Table (2). Table (4). Results of the Proposed algorithm for Table 2 Possible fault locations 4th possible

3rd possible fault location

fault location

Second possible fault location

First possible fault location

Main fault location

Line type

1-2, 1.281e+08 2-3, 1.509e+08 3-9, 2.203e+07 5-11, 1.576e+08 1-2, 2.414e+07 2-3, 1.730e+08 3-9, 1.813e+07 5-11, 1.295e+08 1-2, 1.639e+08 2-3, 1.390e+08 3-9, 1.919e+08 5-11, 2.477e+07

1-2

Single phase to ground

Index of the proposed method -

-

-

-

-

-

-

-

4-10, 2.414e+07 4-5, 1.900e+08 -

3-4, 2.323e+07 5-6, 1.914e+08 -

-

-

-

-

-

4-10, 2.497e+07 4-5, 1.660e+08 -

3-4, 1.849e+07 5-6, 1.409e+08 -

-

-

-

-

4-10, 2.047e+08 4-5, 2.870e+07

3-4, 1.934e+08 5-6, 2.577e+07

-

-

-

2-3 3-9 5-11 1-2 2-3

Two phase to ground

3-9 5-11 1-2 2-3

Three phase to ground

3-9 5-11

Results of distance estimation for different fault types in different fault distances along with different resistances are shown in Table (2) and results of the proposed method are investigated and presented in Table (4) (for all tests in Table (2)). For the analysis of the results, it can be said that for branch (1-2), since the possible location is only one branch and this branch is the real fault location, the proposed method has detected real fault location properly. The test is performed again by increasing fault resistance in two other steps and the results are satisfactory. Tests of branch (2-3) are also conducted with one possible fault location, like the previous branch and the proposed method detects fault location correctly. Next, branch (3-9) is tested. The results of this test show that three branches are detected as possible fault locations. Then, the proposed method is executed to detect the fault section and index of each determined possible fault location. As can be seen in the results given in Table (4), the proposed method performed well, and true detection of real location is achieved. In the following, the test is performed for branch (5-11) and the results are satisfactory. Considering the results obtained from analysis of the proposed method in Table (4), it can be concluded that the proposed method performs well for different types of fault, distances and resistances. Next, the efficiency of the proposed method for different types of fault with different starting angles is investigated. Results obtained from Table (3) are tested again and presented in Table (5).

Table (5). Results of the Proposed Algorithm for different Types of fault with different starting angles Possible fault locations th

4 possible fault location

rd

3 possible fault location

Second possible fault location

First possible fault location

Main fault location

1-2, 2.080e+08 2-3, 2.006e+08 3-9,

1-2

Line type

Index of the proposed method -

-

-

-

-

-

-

4-10,

3-4,

2-3 3-9

Single phase to ground

-

1.776e+08 4-5, 1.429e+08

2.046e+08 5-6, 1.247e+08

1.606e+08 5-11, 1.082e+08

-

-

-

-

-

-

-

4-10, 1.666e+08 4-5, 1.831e+08

3-4, 1.082e+08 5-6, 1.547e+08

1-2, 2.111e+07 2-3, 1.030e+08 3-9, 9.595e+07 5-11, 1.390e+08

-

-

-

-

-

-

-

4-10, 3.221e+07 4-5, 2.163e+008

3-4, 2.836e+07 5-6, 1.846e+008

-

-

5-11 1-2 2-3

Two phase to ground

3-9 5-11

1-2, 4.218e+07 2-3, 3.363e+07 3-9, 9.072e+06 5-11, 1.801e+008

1-2 2-3

Three phase to ground

3-9 5-11

As can be seen in Table (5), the proposed method detects real fault location among possible fault locations properly. The description of this table is similar to Table (4). In the following, the results of testing the proposed method on the real simulator of the power network in the central laboratory of electrical power of Persian Gulf University is presented. 3.2.3. Case Study on Real Simulator of Power Network A power network simulator is used, which is, in fact, a laboratory device. A diagram is designed for this simulator to describe its components and sections. The single-line diagram is represented by Figure (11). Analysis of the diagram shows that the simulator has 7 sections and a 270 km electric current path. Details and complete information on this device are presented in Table (6). Figure (12) shows real image of this simulator. 3

1 2

7

4

DG

6

5

Figure (11). Single-line diagram of the test feeder of the simulator

In this step, results are analyzed by creating a fault in the system and testing the proposed method.

8

Table (6). Specification of Simulator Feeder [23].

branch distance (km) Line type load

1-2

2-3

2-4

4-5

2-7

4-6

7-8

15km

15km

15km

15km

70km

70km

70km

ACSR118.5

ACSR118.5

ACSR118.5

ACSR-226

ACSR-226

ACSR-226

--

--

ACSR118.5 Constant, 315 kVA, 0.8 lag

--

Constant, 500kVA, 0.8 lag

Static 400 kVA, 0.8 lag

Static 500 kVA, 0.85 lag

Distribution system is 20Kv

Figure (12). Real image of the power simulator in central power laboratory, Persian Gulf University

By creating faults in different distances and fault resistance of 50 ohms, the results are presented in tables (7), (8), (9), and (10). Table (7) shows results obtained from testing single-phase fault in the simulator network. Table (7). Results of testing the proposed method on simulator network with single-phase fault 4th possible fault location

Possible fault locations 3rd possible Second possible fault location fault location Index of the proposed method

-

-

-

-

-

-

7-8, 89.32 km 3.3168e+07,×

-

-

2-4, 20.4 km 1.6380e+08,× 4-5, 89.32 km 3.8381e+07,× 4-6, 91.35 km 2.8119e+06,×

First possible fault location

1-2, 8.12 km 2.0239e+07,ok 2-3, 20.4 km 1.4420e+08,ok 4-6, 89.32 km 3.5673e+06,ok 7-8, 91.35 km 2.7887e+06,ok

Main fault location

Line type

1-2, 8 km 2-3, 20 km 4-6, 88 km 7-8, 90 km

single-phase fault

Analysis of the results obtained from the proposed method shows acceptable performance in determining the main fault location. For more investigation, the two-phase fault is used, and the results are given in Table (8).

Table (8). Results of testing the proposed method on simulator network with two-phase fault 4th possible fault location

Possible fault locations 3rd possible Second possible fault location fault location

First possible fault location

Main fault location

Line type

Index of the proposed method -

-

-

-

-

-

7-8, 93.09 km 1.9008e+08,×

-

-

1-2, 10 km

2-4, 24.61 km 2.4976e+07,× 4-5, 93.09 km 1.9124e+08,×

1-2, 10.7 km 1.9124e+08,ok 2-3, 24.61 km 2.3231e+07,ok 4-6, 93.09 km 1.7009e+08,ok

4-6, 98.44 km 1.5876e+08,×

7-8, 98.44 km 1.3821e+08,ok

7-8, 92 km

2-3, 23 km 4-6, 87 km

two-phase fault

Results obtained in this table show the efficiency of the proposed method. The test is performed for three-phase to ground fault and the results are shown in Table (9). Table (9). Results of testing the proposed method on simulator network with three-phase fault 4th possible fault location

Possible fault locations 3rd possible Second possible fault location fault location

First possible fault location

Main fault location

Line type

Index of the proposed method -

7-8, 97.18 km 2.6544e+08,× -

1-2, 12 km

2-4, 28.25 km 5.9828e+07,× 4-5, 97.18 km 2.3279e+08,×

1-2, 13.56 km 6.9807e+07,ok 2-3, 28.25 km 5.5687e+07,ok 4-6, 97.18 km 2.2123e+08,ok

4-6, 99.35 km 3.4334e+08,×

7-8, 99.35 km 5.1241e+07,ok

7-8, 95 km

-

2-3, 25 km 4-6, 86 km

three-phase to ground fault

The results of testing the proposed method for three-phase fault are given in Table (10). Table (10). Results of testing the proposed method on simulator network with three-phase fault 4th possible fault location

Possible fault locations 3rd possible Second possible fault location fault location

First possible fault location

Main fault location

Line type

Index of the proposed method -

7-8, 98.81 km 2.8316e+07,× -

1-2, 14 km

2-4, 29.4 km 2.0465e+08,× 4-5, 98.81 km 2.9217e+07,×

1-2, 14.8 km 1.0828e+08,ok 2-3, 29.4 km 1.7762e+08,ok 4-6, 98.81 km 2.7590e+07,ok

4-6, 101.2 km 4.4707e+07,×

7-8, 101.2 km 4.0870e+07,ok

7-8, 99 km

-

2-3, 28 km 4-6, 95 km

three-phase fault

Finally, considering the results in tables (7)-(10), the efficiency of the proposed method on the network simulator test is verified. Table 11 compares the method proposed in this study with methods from other references. The error of fault location for different methods are compared for 100-ohm fault resistance. As we can see the proposed method is the most accurate among those supporting DGs in distribution networks.

Table 11. Comparison of the proposed fault location algorithm with state-of-the-art algorithms

Line Model

Load Model

Locating Fault Types

Tested System

Feeder Normal Unbalanced Operation

Max. Value of Tested Fault Resistance Reported in the Original Article (Ω)

SLM

Static

L_G

IEEE 13 Bus



150

No

Yes

10.6%

𝜋 Model

Static

All

IEEE 34 Bus



25

No

Yes

1.8%

𝜋 Model

Static

All

IEEE 11 Bus



100

No

Yes

1.5%

DPL

Static

All

IEEE 11 Bus And RPSS



150

Yes

No

0.4%

SLM

Static

All

98-node real-life



50

No

Yes

10.6%

SLM

Static

All

IEEE 34 Bus



10

No

Yes

10.4%

DPL

Static

All

IEEE 11 Bus And RPSS



150

Yes

Yes

0.47%

Items

Authors

Gord et al. Reference [12] Alwash et al. Reference [10] Dashti et al. Reference [11] Gord et al. Reference [23] Bahmanyar et al. Reference [30] Moravej et al. Reference [31] Proposed Method

Real Fault Section Estimation

in the Presence of DGs

Errors % For 100 ohm fault resistance

DPL: Distributed Parameter Line, RPSS: Real Power system simulator, SLM: Short Line Model

It is important to consider high fault resistance when analyzing fault location methods. As it is can be seen from Table 11, only the proposed method in this paper, the method proposed in [23], and the method presented in [12] have been studied for fault resistances up to resistance of 150 ohms. However, among these three, only the proposed method supports networks with DGs. Table 12 compares the success rate of the fault section estimation methods for 100-ohm fault resistance introduced at different fault distances in different sections. It is clear from this table that the method proposed in this article has the highest success rate for fault section estimation among all those supporting DGs. Table 12. Comparison of the proposed fault selection estimation method with other state-of-the-art methods

Reference

Dashti et al Reference [32]

Dashti et al Reference [33]

Bahmanyar et al. Reference [30]

Moravej et al. Reference [31]

Deljoo et al. Reference [34]

Daisy et al. Reference [35]

Proposed Method

Support for DG

No

No





No

No



The Success Rate of Real Fault Section Estimation

It is depended to the number of protective devices and their settings Normally: 85%

It is depended to the number of protective devices and their settings Normally: 85%

89.4%

89.6%

96.8%

97.8%

98.8%

While in the performed tests, only one DG is used, the method is applicable for distribution networks with more DGs. The proposed method employs the information of the terminals in which DGs are installed and the information stored at the beginning of the feeder. Information of the DGs being available in terminal of the sections in which DGs are installed, makes DGs act like a source at the beginning of the feeder where calculations are presented in the paper. This property helps the proposed method to be applicable for networks with higher number of DGs following the same procedure as the one presented in [11]. More details on how to handle fault location for distribution networks in presence of multiple DGs can be found in [11].

4. Conclusion Nowadays we are witnessing an increased use of DGs in electric energy DNs. The problem of fault location is more challenging in the presence of DGs. In this paper, a novel method for an accurate fault location in DNs has been proposed. The proposed method is designed as a two steps approach. In the first step, fault distance is estimated using information stored at the beginning of the feeder and on nodes in which DGs are located. In the second step, this distance is used to determine sections which might be candidates for real fault location. Finally, an innovative algorithm is executed to determine accurate fault location through analysis of fault frequency component of the transient created in the voltage waveform. The proposed method considers distributed line model and presence of DGs. The results obtained from MATLAB simulation, as well as the experimental studies in power simulator, confirm a satisfactory performance. The proposed method solves issues related to its other state-of-art counterparts and is promising for practical applications due to its simplicity and does not require specific equipment, as well as complex computations.

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Highlights 

Fault location in Distribution Networks in the Presence of DGs is addressed.

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The proposed method is a novel two-steps approach.

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The method improves the state-of-art approaches in terms of accuracy.

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The method improves the state-of-art approaches in terms of the success rate.

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A good performance is confirmed in simulation and in the laboratory envirement.