The permanency of highly-reliable power supply is a core trait of an electric power transmission network. A transmission line is the main part of this network through which power is transmitted to the utility. These lines are often damaged by accidental breakdowns owing to different random origins. Hence, researchers are trying to detect and identify these failures at the earliest to avoid financial losses. This paper offers a new real-time fast mathematical morphology-based fault feature extraction scheme for detection and classification of transmission line faults. The morphological median filter is exploited to wrest unique fault features which are then fed as an input to a decision tree classifier to classify the fault type. The acquired graphical and numerical results of the extracted features affirm the potency of the offered scheme. The proposed scheme is 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 on 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 intricacy, in less than a quarter of a cycle.

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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.

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