High-impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm

High-impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm

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This study presents a fast, secure and reliable algorithm for the detection and classification of power system disturbances including high-impedance faults (HIFs). The proposed algorithm utilises mathematical morphology (MM) techniques, where the non-linear MM characteristics are exploited by strategic cascading of appropriate filtering functions to form a multistage morphological fault detector (MFD) for the extraction of features necessary for the characterisation of HIFs. The target features of the HIF are the randomness and arc extinction and re-ignition/unsymmetrical characteristics. The reliability and robustness in the extraction of the desired HIF features are enhanced by a weighted convex structuring element designed based on the attributes of power system signals. The performance of the proposed algorithm is tested under different types of disturbances including cases of HIFs on different contact surfaces. Moreover, the effectiveness of the algorithm is tested under noise condition to demonstrate its performance of the proposed MFD. All tests are simulated using IEEE13 bus test system.


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