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Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults

A transmission line is the main commodity of power transmission network through which power is transmitted to the utility. These lines are often swayed by accidental breakdowns owing to different random origins. Hence, researchers try to detect and track down these failures at the earliest to avoid financial prejudice. This paper offers a new realtime mathematical morphology based approach for fault feature extraction. The morphological open-close-median filter is exploited to wrest unique fault features which are then fed as an input to support vector machine to detect and classify the short circuit faults. The acquired graphical and numerical results of the extracted fault features affirm the potency of the offered scheme. The proposed scheme has been verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification in real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational complexity, in less than quarter of a cycle.