Fault Location Estimation on Transmission Lines using Neuro-Fuzzy System

• Main Authors: Ahmad, A.R (Author), Ahmad, F. (Author), Balfagih Z. (Author), Halabi W. (Author), Hashim, M.N (Author), Ibrahim, M.N (Author), Ibrahimi T. (Author), Lytras M. (Author), Mustari, M.R (Author), Osman, M.K (Author), Rambo K. (Author), Sarirete A. (Author), Uddin M. (Author), Visvizi A. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2019
• Subjects: Adaptive network based fuzzy inference system; ANFIS; Conventional methods; Electric fault location; Electric grounding; Electric lines; Expert systems; fault location; Fault location estimation; Feature extraction; Fuzzy expert systems; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Gaussian distribution; Gaussian noise (electronic); Gaussian process regression; Intelligent systems; Learning algorithms; linear regression; Linear regression; Location; MATLAB; Mean square error; Phase-to-ground faults; Regression analysis; Single phase to ground faults; transmission line
• Online Access: View Fulltext in Publisher; View in Scopus

 Accurate fault location on transmission line is important in ensuring consistent and reliable operation of the power deliver to long distance destination. Conventional methods for locating fault on transmission lines based on travelling wave and impendence-based methods usually suffer from large error due to the complexity of fault modeling on different type of faults. In this paper, an intelligent system for detection of fault location on transmission line using a hybrid model that integrates artificial neural network (ANN) and fuzzy expert system called Adaptive Network-Based Fuzzy Inference System (ANFIS) is proposed. First, a three phase transmission lines is modeled and various types of faults are generated using MATLAB/Simulink. Then, the faulted current signal is segmented from the faulted transmission. Next, feature extraction is performed to obtained information from the faulted current signal. In this study, the extracted features are mean, standard deviation, energy, peak-to-peak and amplitude value. Feature selection is then applied to select important features that correlate with the fault location. For single-phase-to-ground fault, peak-to-peak value and energy is used. Meanwhile, for the line-to-line and double-phase-to-ground faults, only peak-to-peak value is used. Finally, ANFIS network is trained to locate the fault occurrence. Simulation results against two regression models; Linear Regression and Gaussian Process Regression indicated that the ANFIS network is superior in locating the fault. The network achieved the lowest mean squared error (MSE) (0.0012 to 0.0022). © 2019 The Authors. Published by Elsevier B.V.