Open AccessDevelopment of a Wavelet – ANFIS Based Fault Location and Identification System for Underground Power Cables
Author(s) -
Rajveer Singh,
Vinay Krishna Gharami
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k7656.0991120
Subject(s) - adaptive neuro fuzzy inference system , wavelet , fault (geology) , electric power transmission , overhead (engineering) , electric power system , reliability engineering , engineering , transformation (genetics) , power transmission , fuzzy logic , reliability (semiconductor) , membership function , transmission (telecommunications) , identification (biology) , computer science , power (physics) , fuzzy control system , artificial intelligence , telecommunications , electrical engineering , biochemistry , physics , chemistry , quantum mechanics , seismology , geology , gene , botany , biology
Transmission lines are the backbone of electrical power systems and other power utilities as they are used for transmission and distribution of power. Power is distributed to the end-user through either overhead cables or underground cables. In the case of underground cables, their propensity to fail in service increases as they age with time. The increase in failure rates and system crashes on older underground power cables now negatively affect system reliability and involve numerous losses. It is therefore easy to realize that the consequences of this trend need to be managed [3]. Identification of the type of defects and their locations along the length of the cables is vital to minimize the operating costs by reducing lengthy and expensive patrols to locate the faults, and to speed up repairs and restoration of power in the lines. In this study, a method that combines wavelets and neuro-fuzzy techniques for fault location and identification are proposed. For this methodology a power transmission line model was developed and different fault locations were simulated in MATLAB/SIMULINK, and, as an input to the training and development of the Adaptive Network Fuzzy Inference System (ANFIS), certain selected features of the wavelet transformed signals were used. Fault index obtained from wavelet transformation are used as input variable for fuzzy input block function. Different membership functions were observed within input block function. As per formulation of rules, for membership function, the output value of the defuzzifier component was decoded to give a crisp value of ANFIS output. ANFIS results were compared with actual values. A comparison of the ANFIS output values and the actual values show that the percentage error was less than 1%. Thus, it can be concluded that the wavelet-ANFIS technique is accurate enough to be used in identifying and locating underground power line faults. Which will help in solving this time taking and tedious problem more efficiently and thereby reducing human effort in finding the type of fault and its location.