Application of artificial intelligence in early fault detection of transmission line-a case study in India

Prashant P. Mawle, Gunwant A. Dhomane, Prakash G. Burade


Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.


Artificial neural network techniques; Fault analysis; Predictive-maintenance; Pre-fault condition; Pre-outage; Reliability calculation

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).