Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier
In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy s...
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671125000269 |
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| author | Ravi Kumar Jalli Lipsa Priyadarshini P.K. Dash Ranjeeta Bisoi |
| author_facet | Ravi Kumar Jalli Lipsa Priyadarshini P.K. Dash Ranjeeta Bisoi |
| author_sort | Ravi Kumar Jalli |
| collection | DOAJ |
| description | In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy sources, in the microgrid network power quality disturbances (PQD) are becoming quite complex and challenging task. 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. The proposed BILSTM classifier captures the temporal features and their long term dependencies from the processed PF data samples and detects single and simultaneously occurring twenty complex power quality disturbances in the grid connected mode and five PQDs during uncertain PV insolence variation and load and capacitor switching during islanded mode of microgrid operation with significant accuracy of 99.90%. |
| format | Article |
| id | doaj-art-7f7d0d772c6140c3bc29c1e743020a7f |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-7f7d0d772c6140c3bc29c1e743020a7f2025-02-09T05:01:44ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100919Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifierRavi Kumar Jalli0Lipsa Priyadarshini1P.K. Dash2Ranjeeta Bisoi3Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India; Department of EEE, GMR Institute of Technology, Rajam, Andhra Pradesh, IndiaSiksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, IndiaSiksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India; Corresponding author.Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, IndiaIn recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy sources, in the microgrid network power quality disturbances (PQD) are becoming quite complex and challenging task. 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. The proposed BILSTM classifier captures the temporal features and their long term dependencies from the processed PF data samples and detects single and simultaneously occurring twenty complex power quality disturbances in the grid connected mode and five PQDs during uncertain PV insolence variation and load and capacitor switching during islanded mode of microgrid operation with significant accuracy of 99.90%.http://www.sciencedirect.com/science/article/pii/S2772671125000269Multiple power quality eventsMicrogridSignal decompositionStacked autoencoderBidirectional LSTM |
| spellingShingle | Ravi Kumar Jalli Lipsa Priyadarshini P.K. Dash Ranjeeta Bisoi Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier e-Prime: Advances in Electrical Engineering, Electronics and Energy Multiple power quality events Microgrid Signal decomposition Stacked autoencoder Bidirectional LSTM |
| title | Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier |
| title_full | Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier |
| title_fullStr | Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier |
| title_full_unstemmed | Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier |
| title_short | Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier |
| title_sort | identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto encoder based bi directional lstm classifier |
| topic | Multiple power quality events Microgrid Signal decomposition Stacked autoencoder Bidirectional LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2772671125000269 |
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