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    Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier by Ravi Kumar Jalli, Lipsa Priyadarshini, P.K. Dash, Ranjeeta Bisoi

    Published 2025-03-01
    “…Thus to design an effective PQD recognition system, this paper proposes a novel time-frequency analysis method based on adaptively fast complementary ensemble local mean decomposition (AFCELMD) technique that decomposes the multicomponent PQD signal into a series of demodulated product functions (PFs). Out of the several PFs the most sensitive one is selected adaptively and used for feature extraction and classification through a deep stacked auto-encoder (dSAE) hybridized with a time-recursive bi-directional long short term memory (BiLSTM) network classifier. …”
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