Development of a Wavelet – ANFIS Based Fault Location and Identification System for Underground Power Cables
Rajveer Singh1, Vinay Krishna Gharami2

1Rajveer Singh*, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India.
2Vinay Krishna Gharami, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India.
Manuscript received on August 17, 2020. | Revised Manuscript received on August 24, 2020. | Manuscript published on September 10, 2020. | PP: 35-41 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K76560991120  | DOI: 10.35940/ijitee.K7656.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Transmission lines are the back bone 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 ofthe cablesis vital to minimize the operating costs by reducing lengthy and expensive patrolsto locate the faults, and to speed up repairs and restoration of power in the lines. In this study, a method that combines wavelets and neurofuzzy techniques for faultlocation and identificationare 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. 
Keywords: ANFIS, Discrete wavelet transform (DWT), Fault location, Fault types, and Underground cables.