An intelligent scheme for categorization and tracing of shunt abnormalities in compensated power transmission network

Abstract

This paper presents an intelligent protection mechanism for series compensated power network. It principally focuses on identifying and locating the fault events in the network by applying transient signal processing and intelligent computing technique. It involves realization of prime characteristic features from the 3-phase post-fault current signal using discrete wavelet transform decomposition. The realized feature sets (i.e. the entropy of DWT coefficients) are utilized as the input data set to the designed classifier and distance appraisal model. The designed classifier and distance estimator model predicts the type of events and its actuating point in the network as their final output. The probabilistic neural network technique based classifier model has been employed in present work for classifying the shunt abnormality events in the compensated power network. For tracing the location of shunt fault in the network, a cascaded-forward network model has been utilized. Various test cases with varying network operating conditions have been performed on two different simulated test networks in MATLAB for evaluating the competency and feasibility of the proposed intelligent protection scheme. The results enlisted after different considered fault scenario, have vindicated the applicability and strength of the proposed intelligent protection mechanism for ascertaining the class and location of actuation of fault events in a compensated power network.