Performance Evaluation of Maximal Overlap Discrete Wavelet Transform Families with Bidirectional Long Short-Term Memory for High Impedance Fault Detection
Keywords:
High Impedance fault, Maximal Overlap Discrete Wavelet Transform, Bi-directional LSTM, Fault detection.Abstract
Maximal Overlap Discrete Wavelet Transform (MODWT) is employed to characterize three-phase current signals for high impedance fault (HIF) detection. However, a few vital issues, like the classification of HIF from non-HIF, have not yet been benefited by MODWT families and Bi-LSTM. Hence, in this paper, the performance of four families of MODWT, namely, Coiflets (coif), Daubechies (db), Fejer-Korovkin (fk), and Symlets (sym) were studied. A radial distribution network was simulated, and three-phase currents were taken during HIF and non-HIF conditions. Further, this paper attempts to identify the best of the four MODWT families and the level of decomposition required to analyze the current signals. The nine statistical features are extracted from the wavelet coefficients, Kruskal Wallis test is carried out to select the best features and fed into the Bi-directional LSTM(Bi-LSTM) classifier. From the results, it was found out that the coif attained the highest classification accuracy for all the levels of decomposition.
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