Neuro-Fuzzy Access For Detection of Faults in An Underground Cable Distribution System
Garima Tiwari1, Sanju Saini2
1Garima Tiwari, Department of Electrical Engineering, DCRUST, Murthal (Haryana), India.
2Dr. Sanju Saini, Department of Electrical Engineering, DCRUST, Murthal (Haryana), India.
Manuscript received on 22 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 569-573 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11030882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1103.0882S819
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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: Nowadays, the interest of power system engineers In Indian Power System has increased towards the use of underground cables with the advent of cross-linked polyethylene (XLPE) insulated cables having high capacity for transmission of power. Underground cables are preferred in the densely populated regions where there is environmental constraint and right of way poses a big problem. The key limitation of underground cable is to locate and detect different types of faults in view of the fact that the cables are lying down under the surface. It is necessary that the fault must be cleared in minimum time on account of protection issues. As conventional methods for detection and classification of faults are time consuming, so, this work uses intelligent techniques for fast and more accurate detection of location and classification of faults in underground cables. Overall work has been performed in three steps, the first step is to develop a MATLAB/Simulink Model of a distribution system using underground cable with a provision to develop fault. In second step, an Artificial Neural Network (ANN) using DWT is used for fault detection & classification. In third step, ANN is hybridized with fuzzy system and discrete wavelet transform (DWT) methods to improve its performance. The training sets of adaptive neuro fuzzy inference system (ANFIS) are energy components of three phases of cable under fault (used as inputs) and fault type or different distances of faults in the cable(used as outputs). All the simulations have been carried out in MATLAB/ SIMULINK environment.
Keywords: Underground Cables, Fault Detection, Adaptive Neuro Fuzzy Inference System (ANFIS).
Scope of the Article: Fuzzy Logics