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Aiming at the problem of low accuracy of the current distribution network load data anomaly detection method, a distribution network load anomaly detection method combining the improved long and short issue memory network and variational autoencoder is proposed. The long and short issue memory network is optimized through the residual structure to improve the feature learning ability, and the optimized long and short issue memory network is replaced with the BP neural network layer (encoding and decoding) of the variational autoencoder, which can better obtain the time correlation of the load data. The superiority of the proposed detection method is verified by the experimental comparison of the conventional AND detection method. The results show that compared with the conventional load data anomaly detection method, the proposed method has a better detection accuracy, with an anomaly detection accuracy of 97.30%, which is 1.70% higher than that of the residual structure without introduction, 7.00% higher than that of the LSTM model, and 4.80% higher than that of the PSO-PFCM model. It can provide a certain reference for the development of distribution network automation.
Email: napstic@istic.ac.cn
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Institute of Scientific and Technical Information of China All rights reserved
Page：Fund：电视剧 Views：Chinese Keywords:Aiming at the problem of low accuracy of the current distribution network load data anomaly detection method, a distribution network load anomaly detection method combining the improved long and short issue memory network and variational autoencoder is proposed. The long and short issue memory network is optimized through the residual structure to improve the feature learning ability, and the optimized long and short issue memory network is replaced with the BP neural network layer (encoding and decoding) of the variational autoencoder, which can better obtain the time correlation of the load data. The superiority of the proposed detection method is verified by the experimental comparison of the conventional AND detection method. The results show that compared with the conventional load data anomaly detection method, the proposed method has a better detection accuracy, with an anomaly detection accuracy of 97.30%, which is 1.70% higher than that of the residual structure without introduction, 7.00% higher than that of the LSTM model, and 4.80% higher than that of the PSO-PFCM model. It can provide a certain reference for the development of distribution network automation.Email: napstic@istic.ac.cn Chinese Keywords:Sign in 周番剧表 Follow the official account 留言板观看记录

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Research on Abnormal Load Detection Method for Distribution Network Based on Improved LSTM-VAE_China S&T Journals

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Email: napstic@istic.ac.cn Institute of Scientific and Technical Information of China All rights reserved Beijing ICP Preparation No. 10027328-24

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