Performance Comparison of MLP and RBF Neural Networks for Fault Location in

Distribution Networks with DGs

Hadi Zayandehroodi*, Azah Mohamed*, Hussain Shareef * and Marjan Mohammadjafari** * Department of Electrical, University kebangsaan Malaysia (UKM), Selangor, Malaysia ** Department of Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

h.zayandehroodi@yahoo.com, azah@eng.ukm.my, hussain_ln@yahoo.com, marjan_mohamadjafari@yahoo.com

Abstract— With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.

Keywords—Fault location; distributed generation (DG); distribution network; radial basis function neural network (RBFNN); multilayer perceptron neural network (MLPNN).

I. INTRODUCTION With increasing reliance on electricity, customers

want reliable power supply with reduced outage time and operating costs. When a fault occurs in a distribution network, it is important to quickly locate the fault by identifying either a faulty bus or a faulty line section in the network. Without locating the faulty section, no attempts can be made to remove the faults and restore the power supply. Fault location in electric power distribution systems still presents many challenges due to its varied topological and operational characteristics. The traditional methods used for locating faults in a distribution network are either by visually identifying a fault in the line, sending a pulse down the circuit and analyzing the reflected wave; or by using the utility outage management system to identify circuit outages. To locate precise fault location, voltages and currents are measured using intelligent electronic devices installed at substations. The traditional fault location method has a drawback in which it is not able to locate faults quickly. To solve this problem, an automated fault location using intelligent data interpretation system is applied. Several artificial intelligence techniques have been developed for automated fault location in distribution systems [1-5]. The intelligent fault location methods locate faults by calculating the fault distances, identifying

the faulted phases and locating the faulty protective devices. These methods, however, do not consider distribution networks with distributed generation (DG). From a technical viewpoint, the presence of distributed generators in a distribution network would result in some conflicts in the operation of the present network because distribution network configuration is no longer radial in structure. The conventional fault location scheme is suitable for locating faults in a system with a single source and radial supply line or with multi-source open loop operation. With DGs in a distribution network, determining the exact location of faults is becoming complicated, as faults are fed by multi-sources. Hence, the existence of DGs in a distribution network poses a difficulty in locating faults in the network. The aim of the research is to develop an accurate and automated fault location method for a distribution network with distributed generators by identifying the faulty line.

Recently, fault location methods have been developed by taking into account the presence of DGs in a distribution network [6]. Zhu & Lu developed a fault location algorithm for a distribution system with DGs by using current measurements [7, 8]. In this method, after a faulted segment is located, islands are formed involving groups of DGs. To match the loads with the DGs generating capability in an island, a load shedding scheme is implemented and a mechanism is required to reconnect the disconnected loads after faults are removed. A method for finding the exact location of faults in a network with DG has been developed by Conti & Nicotra [9]. It is based on programming and requires a telecommunication control system. Another fault location method estimates the impedance of fault by measuring current and voltage at a substation [10]. In this method, the fault location performance is inaccurate when the DG is located upstream of the fault section with the impact being more severe for synchronous machine based DG. Jiao et al. [11] proposed a fault location method for a distribution network with DGs by identifying the direction of an asymmetrical fault based on negative sequence current scalar product. An asymmetrical fault line searching and locating scheme is then developed by combining the fault direction distinguishing method with its communication system. The previous fault location method for a distribution

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network with DGs was developed using multi layer perceptron neural network (MLPNN) [12-14]. Considering the structure and training algorithm of the MLPNN, it takes considerable training time and therefore it is not suitable for fast fault location. Zayandehroodi et al. proposed an automated fault location method for identifying the exact faulty line in distribution network with DGs by using the radial basis function neural network (RBFNN). The proposed method can increase network reliability and decrease the total down time of the system [15].

This paper presents an automated fault location scheme for a distribution network with DGs using the radial basis function neural network (RBFNN) which is considered as a better neural network model for solving engineering problems. The proposed scheme determines the fault type by normalizing the fault current of the main source whereas the distance of faults from each source is determined by using RBFNNs. A comparison is also made between the RBFNN and the conventional MLPNN for locating faults in a power distribution system with DGs.

II. MLP AND RBF NEURAL NETWORKS Multilayer perceptron (MLP) neural network models

are used in most of the research applications in medicine, engineering, mathematical modeling, etc. [16]. In MLPNN, the weighted sum of the inputs and bias term are passed to activation level through a transfer function to produce the output, and the units are arranged in a layered feed-forward topology. MLPNN which is trained with the standard back propagation algorithm is considered very slow. Due to the great amount of calculations required in MLPNN and low convergence speed of the back propagation algorithm, several neural network models have been developed to increase its convergence speed.

The radial basis function (RBF) neural network is a special type of artificial neural network with characteristic topology different than the MLPNN. RBFNN is based on supervised learning and consists of three layers, namely, input layer, hidden layer and output layer. The input layer feeds the input values to each of the neurons in the hidden layer. This consists of neurons with radial basis activation functions. The output layer consists of neurons with linear activation function. A generic architecture of an RBFNN with k input and m hidden neurons is shown in Fig.1.

Figure 1. A generic architecture of the RBFNN

In the training of the RBFNN, the following computations are considered. When the network receives

a k dimensional input vector X, the network computes a scalar value using,

∑=

+==m

i iDiwwf(X)Y1

)(0 ϕ (1)

where w0 is the bias, wi is the weight parameter, m is the number of nodes in the hidden layer and (Di) is the RBF.

In this study, the Gaussian function is used as the RBF and it is given by

)exp()( 2

2

σφ i

iDD −

=

(2)

where σ is the radius of the cluster represented by the center node, Di is the distance between the input vector X and all the data centers.

The Euclidean norm is normally used to calculate the distance, Di which is given as

(∑=

−=k

jjiji CXD

1

2)

(3)

where C is a cluster center for any of the given nodes in the hidden layer [17].

The implementation procedures in the training of the RBFNN are presented as follows:

• Step 1: Obtain input data and target data from the simulation.

• Step 2: Assemble and preprocess the training data for the RBFNN.

• Step 3: Create the network object and train the network until condition of network setting parameters are reached.

• Step 4: Test and conduct regression analysis. • Step 5: Stored the trained network. Steps (1–5)

are offline processes. • Step 6: Preprocess the new input before they are

subjected to the trained network to obtain required data.

III. IMPLEMENTATION OF FAULT DIAGNOSIS IN DISTRIBUTION NETWORKS WITH DG UNITS USING

RBFNN AND MLPNN An important consideration in fault diagnosis of

distribution networks is the determination of the fault type and location of faults occurring in its protection zone. In this work, a sample distribution network with two DG units is considered as a test system for simulation. The fault currents of the main source are normalized and used for determining the various types of faults. For determining the fault location in a test distribution system with DGs, RBFNN and MLPNN have been developed in which the training data sets for the RBFNN and MLPNN are generated by performing short circuit simulations at all line sections considering four types of faults and creating faults at every 100 meter of each line.

A. Test System description To verify the performance of the proposed fault

location method using the RBFNN and MLPNN, the 22 bus, 20 kV distribution network with 2 DG units as shown in Fig. 2 is selected as the test system. The test system data can be found in [15]. In the study, simulations were

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Figure 2. Single line diagram of the test system

14.0.516 software for generating the neural network training data. Training and testing of the RBFNN and MLPNN were implemented in MATLAB.

In this work, the number of the input neurons is 9 which consists of 3 phase short circuit currents of each source (IS, IDG1, IDG2) and the output neuron is 3 comprising of the fault distances from the three sources. The target/output vector for the training is obtained from the simulation. In the training of the neural networks, the number of iterations is limited to 1000.

B. Identifying the fault type based on current normalization

To identify the various fault types, the 3 phase currents of the main source from the feeding substation are used. The three phase output fault currents at the main source or the feeding substation are normalized using,

maxII

normalI =

(4)

where I is the fault current and Imax is the maximum fault currents for each type of fault.

Based on the normalized three phase fault currents, the fault types are classified as shown in Table 1[18]. From the table, “1”, “-1” and “0”, indicate that a fault occurs in the phase, a fault occurs in the phase but the short circuit current is in the opposite direction and no fault, respectively.

C. Identifying fault location Using RBFNN and MLPNN

After identifying the fault type, its location should be

determined. In this study, RBFNN and MLPNN were developed for the various fault types, namely, single phase to ground fault (1Ph-G), phase to phase fault (2Ph), two phase to ground fault (2Ph-G) and three phase fault (3Ph). Therefore, fault currents of the main source and all DGs are taken as the inputs for training RBFNN and MLPNN. When a fault occurs in the distribution network, fault type can be determined first by normalizing the 3-phase currents of the main source. Then after recognizing the fault type, the RBFNN and MLPNN corresponding to a particular fault type are activated to show the exact fault location. All procedures of the computation mentioned above is illustrated in Fig. 3.

Figure 3. Implementation of the proposed fault location

TABLE I FAULT TYPE CLASSIFICATION DATA

Fault type Ia Ib Ic

1-phase to ground

Ag 1 0 0

Bg 0 1 0

Cg 0 0 1

phase to phase

AB 1 -1 0

AC 1 0 -1

BC 0 1 -1

2-phase to ground

ABg 1 1 0

ACg 1 0 1

BCg 0 1 1

3 -phase ABC 1 1 1

carried out by using the DIgSILENT Power Factory

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IV. TEST RESULT AND DISCUSSION To show the performance of the proposed fault location

method, a simulation of the aforementioned 22-bus system shown in Fig. 2 is conducted. In neural network, the mean square error (MSE) is used to determine the accuracy of the neural networks. Table II shows the training performances of the RBFNN and MLPNN for all types of faults. From the table it is clear that value of the MSE and its related epoch for RBFNN is less than MLPNN for the same goal. Therefore, RBFNN has better performance with smaller error for identifying the fault location.

The various fault types have been selected randomly for testing the neural networks. It is noted that each fault sample occurs at a random percentage of length of distribution lines in the studied network. The different cases of fault conditions are presented as:

1. Four types of fault at 120 meters of length of the line 1 from the bus 1 to the bus 2

2. Four types of faults at 360 meters of length of the line 13 from the bus 13 to bus 14

3. Four types of faults at 850 meters of length of the line 18 from the bus 11 to bus 19

4. Four types of faults at 570 meters of length of the line 21 from the bus 21 to bus 22

Table III shows the RBFNN and MLPNN outputs for

various type of faults occurring in different lines of the studied network. Comparing the results shown in Table III, it can be noted that the highest error from RBFNN and MLPNN are about 10 and 20 meters, respectively for the difference between the actual and estimated distances of fault distances from the main source (Ds) and all DGs (DDG1,DDG2). From Tables II and III, it is obvious that RBFNN is faster and more accurate in determining fault location compared to MLPNN. This proves that the use of RBFNN for fault location in distribution networks with DGs can improve network reliability and decrease total down time of the system.

V. CONCLUSION An automated fault location method in a distribution

system with DGs using RBFNN has been presented. In the proposed method, normalized fault currents of the main source are used for determining the fault type and RBFNN has been developed for determining the fault location in a test distribution system with DGs. From the case study considering 2 DG units in a test distribution system, the results of the proposed RBFNN fault location method are satisfactory. The test results also showed that the RBFNN is more accurate in locating faults compared to that of MLPNN.

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TABLE II TRAINING PERFORMANCE OF THE RBFNN AND MLPNN

Fault type Goal RBFNN MLPNN

MSE Epoch MSE Epoch

1 Ph-G 0.0001 5.58e-005 30 2.93e-003 120

2 Ph 0.0001 7.83e-005 34 1.08e-004 100

2Ph-G 0.0001 9.07e-005 29 1.29e-003 100

3 Ph 0.0001 7.85e-005 30 1.39e-002 110

TABLE III TESTING PERFORMANCE OF THE RBFNN AND MLPNN FOR LOCATING

FAULTS

No Fault type

Neural network type

RBF MLP RBF MLP RBF MLP

Ds (km)

Ds (km)

DDG1 (km)

DDG1 (km)

DDG2 (km)

DDG2 (Km)

1

1 Ph-G 0.130 0.139 2.889 2.898 7.883 7.899 2 Ph 0.129 0.141 2.881 2.866 7.872 7.864

2 Ph-G 0.126 0.135 2.889 2.871 7.879 7.872 3 Ph 0.128 0.131 2.879 2.860 7.875 7.865

Actual 0.120 2.880 7.880

2

1 Ph-G 6.366 6.378 7.361 7.365 6.367 6.378 2 Ph 6.357 6.379 7.363 7.369 6.363 6.369

2 Ph-G 6.365 6.342 7.367 7.378 6.365 6.375 3 Ph 6.358 6.351 7.352 7.342 6.364 6.377

Actual 6.360 7.360 6.360

3

1 Ph-G 4.843 4.869 5.848 5.868 3.149 3.132 2 Ph 4.852 4.870 5.850 5.862 3.156 3.139

2 Ph-G 4.856 4.864 5.851 5.830 3.155 3.159 3 Ph 4.853 4.861 5.847 5.867 3.159 3.168

Actual 4.850 5.850 3.150

4

1 Ph-G 7.551 7.569 8.556 8.570 0.428 0.449 2 Ph 7.557 7.567 8.557 8.537 0.432 0.445

2 Ph-G 7.557 7.564 8.545 8.531 0.435 0.439 3 Ph 7.554 7.561 8.560 8.569 0.439 0.416

Actual 7.550 8.550 0.430

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Technologies, 2008. ICSET 2008. IEEE International Conference on, 2008, pp. 164-168.

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AUTHOR BIOGRAPHIES

Hadi Zayandehroodi is Power System Engineering PhD candidate in the Department of Electrical, Electronic and System Engineering, National University of Malaya (UKM). He received his B.Sc. degree in Power System Engineering in 2003 from Azad University of Tehran, Iran, and his M.Sc. degree in Power System Engineering from Tarbiat Modares University, Tehran, Iran, in 2005 and 10 years of experience in power

system companies in Iran. He is interested in designing protection systems for power systems, especially distribution systems including distributed generation as well as distribution system restructuring and developing software applications for electrical distribution systems.

Azah Mohamed received her B.Sc from King’s College, University of London in 1978 and M.Sc and Ph.D from Universiti Malaya in 1988 and 1995, respectively. She is a professor at the Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia. Her main research interests are in power system security, power quality, distributed generation and artificial intelligence. She is a senior member of IEEE.

Hussain Shareef received his B.Sc with honor from IIT, Bangladesh, MS degree from METU, Turkey, and Ph.D from Universiti Teknologi, Malaysia. He currently is a faculty member at the Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia. His current research interests are power system deregulation and power quality.

Marjan Mohammadjafari is Industrial Engineering PhD candidate in the Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya (UM). She holds Master of Industrial Engineering from University of Zahedan in Iran and Bachelor of Electrical Engineering from university of Kerman, Iran and 7 years of experience in project manager in industrial companies in Iran.

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