Fault Detection in Double Circuit Transmission Lines Using ANN

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1 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: Fault Detection in Double Circuit Transmission Lines Using ANN Chhavi Gupta, Chetan Bhardwaj 2 U.T.U Dehradun, Uttarakhand,Dehradun-2489, India 2 Department of Electrical Engineering, DIT University, Dehradun-2489, India chhavi.gupta98@gmail.com, 2 chetan.eee99@gmail.com Abstract- This paper discuss the fault detection in high voltage transmission line using artificial neural network technique. The problem of faults in a transmission line has been going for a very long time. It has been one of the major concerns of the power industry. Normally, protective relays, recording devices and special control and protection software systems are responsible for detecting the fault occurrences and isolating the faulted portion from the system. Thus it is necessary for the faults to be detected quickly and precisely. It is also equally important to know the details about the fault that has occurred so that it can be corrected soon. This paper focuses on detecting faults on High Voltage electric power transmission lines. Fault detection have been achieved by using artificial neural networks. Backpropagation algorithm has been employed using feedforward networks for each of the three phases in the Fault detection process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Taking this into account, models for conventional Transmission line to generate voltage and current parameter is developed in MATLAB/SIMULINK. A selected simplified power network is used to simulate all possible fault scenarios and to generate test cases. From generated data test sets, ANN is trained and tested for fault detection and fault classification. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient for detecting on High Voltage transmission lines and achieve satisfactory performances. Key words: Double circuit transmission line, ANN, Fault detection..introduction The power transmission over a utility company network is a complicated process involving a number of generating plants, transmission lines, distribution and substations. Electric power is normally generated at -25kV in a power station. It is necessary to transmit over long distances; it is then stepped-up to HV / EHV / UHV levels. Power is generally transmit through a transmission network have high voltage lines. [5] High Voltage transmission lines utilized in modern power systems to increase reliability power transfer, and security for the transmission of electrical energy. The different configurations of double circuit transmission lines with the effect of mutual coupling make their protection a problem. The vast majority of faults (over 9%) [] are of the single line to ground type. The appropriate percentages of occurrences various faults are listed below: Single line to ground fault 7-8%, Line-Line to ground fault - -7%, Line- Line fault 8-%,three phase 2-3%.The causes of temporary faults include lightning, conductors slapping together in the wind, tree branches that fall across conductors and then fall or burn off and insulator flashovers caused by pollution. Faults that are normally temporary can turn into permanent faults. data and exciting results were noticed by the proposed Neural Network. The fault detection tested with problem being a major part of transmission and distribution system automation is selected as the topic of in this paper. In this, a method for detection of fault in high voltage transmission systems is devised. The various practical constraints, which are the unique characteristics of the transmission system are considered in the fault detection and classification problem. The work broadly covers: Development of a new approach for fault detection in transmission systems using Feed Forward Neural Networks and Neural Network as a detector, using the measurements available at the substation. The objectives of this paper is to develop the proposed 5-bus system model of transmission system for generating data sets for ANN technique under steady state and different faulty states like line to ground fault, line to line fault, double line to ground fault, and three phase fault etc and to develop an Artificial Neural Network (ANN) model from data sets obtained by the transmission model. The algorithm used in this paper employs the fundamental components of voltage and current signals. A model of 25KV 9

2 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: transmission line is developed and simulated in MATLAB/SIMULINK for generating training data sets for ANN. The faults are more easily detecting using ANN in a simple and efficient way. 2. SYSTEM CONFIGURATION To gauge the performance of the proposed neural network-based fault detector, a 25 kv, 2 km transmission line extending between two sources has been considered in this study as shown in figure. Fig. 2 System model simulated in MATLAB/Simulink The first step in the process is fault detection. 3. NETWORK SIMULATION Fig. The system under consideration The transmission line is represented by distributed parameters and the sampling frequency of the line parameters is taken into account [3].The values of the three-phase voltages and currents are measured at 5 bus system and the resulting data set is ultimately fed into the neural network as inputs. A simulink model of the transmission line model shown in figure 2 which has been used to generate the entire set of training and testing data for Neural Network. There have been different fault cases simulated for the purpose of fault detection. The network is trained using znntrain. This function calls ANN functions included in the MATLAB NN toolbox. The functions called are newff and nftool. The newff function creates feedforward network and returns a network object. The train function trains the network created.the syntax of newff function can be read from MATLAB help. The one used in this work only uses some of its parameters namely newff (input, [N, N2, Nj], {F, F2, Fj}, training algorithm). The above function creates a feedforward network with j layers (input layer not involved). The Tolal numbers of nodes withevery layer are N, N 2, and N j with each layer having activation functions F, F 2, and F j respectively. 4. FEEDFORWARD NETWORKS Where there is no feedback connection involved in the network is called feed forward network and hence the information travel is unidirectional.a feedforward network with N input and KR output signals is shown in Fig 3. The computation process in the ith layer can be described by the following equation (3) q i = f i ( W i g i- ) Where q i = [q i, q i 2, q i 3.q n i ] T is the signal vector at the output of the ith layer. 92

3 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: Figure 3. Structure of a two-layered feedforward network W i W i W i i- W i 2 W i 22 W i 2i- W i- = W i ni2 W i nio W i ni is the weighing matrix between the ( i-)th the ith layer. A is the vector containing the input signals, f (i) is the activation function of the neurons in the ith layer and R is the number of processing layers. All the neurons in a particular layer are assumed to be similar in all aspects and the number of hidden layers can be more than one and is usually determined by the purpose of the neural network. The output of the processed neural network is represented by the output vector Y.[4] Y= [ y y 2.. y nr ] 5. PROPOSED FAULT DETECTOR BASED ON NEURAL NETWORKS Functional blocks of the proposed fault detector are shown in Figure.4 Voltage signals at the transmission line end S (relay location) will be acquired by the relay at buses. After preprocessing, It fed to the fault detector (FD) to detect a fault, and if the fault is detected by FD, the fault classifier (FC) estimates the which type of the fault occur in the transmission line. The proposed fault detector (FD) is designed to indicate the presence or absence of a fault. The occurrence of the fault is classifying. Figure 4: Major blocks constituting the fault detector [7] 5. Design Process of Ann Fault Detector The design process of the ANN fault detector goes through the following steps:. Preparation of a suitable training data set that represents cases that ANN needs to learn to process further. 2. Selection of a suitable ANN structure for a given application for better output. 3. ing the ANN with suitable data. 4. Evaluation of the trained ANN using test patterns until its performance is satisfactory or error reduced.[5] Fault Detector In order to build up an ANN, the inputs and outputs of the neural network have to be defined for recognition of pattern. The inputs of the network must provide a true representation of the situation under consideration. The generation process of input patterns to the ANN fault detector (FD) [6]. Inputs and Outputs In order to build up an ANN, the inputs and outputs of the neural network have to be defined for pattern recognition. The inputs to the network should provide a true representation of the situation under consideration. The process of generating input patterns to the ANN fault detector (FD). The input (Voltage) patterns are generated as a string of samples corresponding to a sampling frequency. These signals as to simulate a continue sampling process (62 samples per 5 Hz cycle). The ANN output is indexed with either a value of in the case of fault present or in the case of fault absence. The input layer gives the three phase current and voltage. The outputs of the hidden layer with the sigmoid activation function are calculated and transferred to the output layer, which is composed of only one neuron. The output value of the neuron in the output layer with the sigmoid activation function calculated to gives the state of the transmission line: (the presence of a fault) or (the non-faulty situation) [8]. 93

4 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: FAULT DETECTION RESULTS AND DISCUSSION Fault detection perform an important role in minimizing the damage to system due to high short circuit fault or short circuit current and fast detection of fault conduces to quick isolation of faulty transmission line from the system, hence protect it from the harmful effects of voltage and current.for the purpose detection, various methods of Multi-Layer Perceptron have been studied. The various factors that play a role in deciding the ideal methods are the network size, the learning strategy employed and the training data set size. The backpropagation algorithm has been decided as the ideal methods. The basic back-propagation algorithm is very slow due to the small learning rates employed, few techniques can significantly enhance the performance of the algorithm. The Levenberg- Marquardt optimization technique is a one such strategy used. The selection of the network size is very vital because this not only reduces the training time but also greatly enhance the ability of the neural network to represent the problem in hand. Unfortunately there is no thumb rule that can dictate the number of hidden layers and the number of neurons per hidden layer in a given problem. 6.ing the Fault Detection Neural Network M ean S q u are d E rro r (m se) Performance is at epoch Epochs Figure 5 Mean-square error performance of the network (6--) Performance is at epoch Epochs Figure 6.Mean-square error performance of the network (6-25-) Performance is at epoch Epochs Figure 7 Mean-square error performance of the network (6-35-) Performance is at epoch Epochs Figure 8 Mean-square error performance of the network (6-4-) 94

5 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: Performance is at epoch Epochs Figure 9 Mean-square error performance of the network (6-5-) Discussion For the fault detection, the network takes in six inputs at a time, which are the voltages and currents for all the three phases for ten different faults and also no-fault case. The output of the neural network is just a yes or a no ( or ) depending on whether or not a fault has been detected. After extensive simulations it has been decided that the desired network has one hidden layer with neurons in the hidden layer. For illustration purposes, several neural networks (with varying number of hidden layers and neurons per hidden layer) that achieved satisfactory performance are shown and the best neural network has been described further in detail. output layer). From the above training performance plots, it is to be noted that very satisfactory training performance has been not achieved by the neural network with the configuration (6 neurons in the input layer, hidden layers with 35 neurons in them respectively and one neuron in the output layer). Figure 8 training performance plots, it is to be noted that very good training performance has been achieved comparison to last one. The overall MSE of the trained neural network is way below the value of.. Hence this has been not chosen as the ideal ANN for the purpose of fault detection.by the neural network with the configuration (6 neurons in the input layer, hidden layers with 35 neurons in them respectively and one neuron in the output layer). It is to be noted that very satisfactory training performance but did not achieved the satisfactory result by this neural network. Figure 9 training performance plots, it is to be noted that very satisfactory training performance has been achieved comparison to result network (6-5-).The overall MSE of the trained neural network is near about the value of.. Hence this has been chosen as the ideal ANN for the purpose of fault detection. ing the Fault Detection Neural Network Figures 5-6 show the error performance plots of neural networks with hidden layers and 25 respectively. The various error performance plots have been shown in Figures 5 9. Figure 5 shows the training performance plot of the neural network 6-- (6 neurons in the input layer, hidden layer with neurons in it and neuron in the output layer). It can be seen that the network did not achieve the desired Mean Square Error (MSE) goal by the end of the training process. Fig 6 shows the training performance plot of the neural network with configuration (6 neurons in the input layer, hidden layers with 25 neurons respectively and one neuron in the output layer). It is to be noted that the neural network could not achieve the MSE goal of. by the end of the training process. Figure 7 shows the training process of the neural network with configuration (6 neurons in the input layer, hidden layers with 35 neurons in them respectively and one neuron in the 95

6 International Journal of Research in Advent Technology, Vol.3, No.8, August 25 E-ISSN: O u t p u t ~ =.9 2 * T a r g e t +. 4 O u t p u t ~ =.9 2 * T a r g e t ing: R= : R= O u t p u t ~ =.9 2 * T a r g e t +. 4 O u t p u t ~ =.9 2 * T a r g e t : R= All: R= Figure.Regression fit of the output s vs. targets for the network (6--) Discussion Once the neural network has been trained, its performance has been tested by three different factors. The first of these is by plotting the best linear regression that relates the targets to the outputs as shown in Figure. The correlation coefficient (r) is a measure of how well the neural network s targets can track the variations in the outputs ( being no correlation at all and being complete correlation). The correlation coefficient in this case has been found to be in this case which indicates excellent correlation. 7. CONCLUSION The objective of this paper is to present a novel approach for detection faults in High Voltage Transmission Line systems using ANN technique considering various practical constraints. Power system Transmission Line model was developed in MATLAB/SIMULINK platform. The proposed Neural Network can be used for a real time approach in the power system analysis. From the results obtained, it can be concluded that the measurements made during fault in a high voltage transmission system contain significant information about the detection of faults. The measurements obtained at the substation are used to detect fault in Transmission systems. The studies carried out in this work show that, for different network configurations, the similar structure of the neural network can be used, and only the weights of the neural networks are updated for different configurations. REFERENCES [] Anamika Yadav and A.S Thoke, Classification of Single Line to Ground Faults on Double Circuit Transmission Line using ANN International Journal of Computer & Electrical Engineering /29; DOI:.7763/IJCEE.29.V.3. [2]Manohar singh, Dr. B.K Panigrahi Department of Electrical Engineering, Indian Institute of Technology,Delhi, India-6 manoharsingh33@ gmail.com [3]Aggarwal, R.K., Xuan Q.Y., Dunn, R.W., Johns, A.T. and Bennett, A Novel Fault Classification Technique for Double-circuit lines Based on a Combined Unsupervised/Supervised Neural Network, IEEE Transactions on Power Delivery, VOL.4, October 999 [4] Rajveer Singh," Fault Detection of Electric Power Transmission Line by Using Neural Network,Asstt.Prof. Deptt. of Electrical EngineeringDecember 22, Jamia Millia Islamia, New Delhi, India [5] Bouthiba T. and Maun J.C., Relais à base de réseaux de neurones pour la protection des lignes de transport à THT.Revue Internationale de Génie Electrique, Vol. 6, No. 3 4, 23 pp [6] Feng Yan, Zhiye Chen, Zhirui Liang, Yinghui Kong, and Peng Li, Fault Location Using Wavelet Packets, Department Of Electric Power Engineering, North China Electric Power University, Baoding Hebei, China, , 22 [7]Anamika Jain, Thoke, A. S., and Patel, R. N.29. Classification of Single Line to Ground Faults on Double Circuit Transmission Line using ANN, International Journal Of Computer And Electrical Engineering, Vol. (No. 2), June 29, [8] Murari Mohan Saha, Ratan Das,PekkaVerho, and Damir Novosel, Review of Fault Location Techniques for Distribution Systems, Power Systems And Communications Infrastructures For The Future, Beijing, September

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