Artificial Intelligence based Faults Identification, Classification, and Localization Techniques in Transmission Lines-A Review
Keywords:
Artificial neural network, Fault identification and classification, Fuzzy Inference system, Hybrid methods, Transmission line, Wavelet transform.Abstract
An overview of the many methods used for fault detection, classification and location in the power system, particularly in transmission lines, is provided in this review, it also includes an experimental result of adaptive neuro-fuzzy inference system -based fault detection , fault classification and fault location. Being in operation outdoor environment, transmission lines are more vulnerable to various faults which may lead to system collapse in severe cases. Therefore, to ensure the reliable and safe operation of power system it is imperative to critically monitor the faults in transmission lines. In this regard, researchers around the globe have developed several techniques and constantly putting efforts to further improve the protection efficacy. The brief yet thorough analysis and comparison of the artificial intelligence-based techniques, hybrid methodologies and most recent approaches in the context of power system faults have been discussed and presented. In addition, the research work and the experimental results of an adaptive neuro-fuzzy inference system-based techniques have also been discussed for IEEE-9 bus system. The mean square error for testing data of ANFIS-based fault detection, classification, is zero and for fault location Mean square error is 5.32km. This piece of work could be helpful in the development of a comprehensive understanding of various artificial intelligence-based techniques within the realm of fault detection, classification and localization in transmission lines.
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