ABNORMAL DATA DETECTION OF NEW DISTRIBUTION NETWORK BASED ON CNN-LSTM HYBRID NETWORK

Wang Bingmei; Zhang Ye; Li Shubin; Hui Qian; Zhang Wenshu

doi:10.19912/j.0254-0096.tynxb.2024-0897

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 243-250. DOI: 10.19912/j.0254-0096.tynxb.2024-0897

ABNORMAL DATA DETECTION OF NEW DISTRIBUTION NETWORK BASED ON CNN-LSTM HYBRID NETWORK

Wang Bingmei, Zhang Ye, Li Shubin, Hui Qian, Zhang Wenshu

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Abstract

In order to improve the accuracy of abnormal data detection of new distribution network with distributed photovoltaic and reduce the false alarm rate of abnormal data detection, this article proposes a new method of abnormal data detection of distribution network based on CNN-LSTM hybrid network. Firstly, aiming at the heterogeneous data collected by the new distribution network, the multi-energy data isomorphism model of the new distribution network is established using the information sensor network of the new distribution network. Then, combining the convolutional neural network with the long-term and short-term memory network, an abnormal data detection method based on CNN-LSTM hybrid network is proposed to determine the relevant indicators that can evaluate the abnormal data detection results of the new distribution network. Finally, referring to the historical data of the new distribution network, the performance of abnormal data detection in the new distribution network using the CNN-LSTM hybrid network and other algorithms is compared and analyzed. The simulation results show that the detection performance of the new distribution network anomaly data based on CNN-LSTM hybrid network is more stable on different data sets and the detection results of abnormal data are more accurate.

Key words

neural network model / long short-term memory / anomaly detection / data processing / distributed photovoltaic / distribution network

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Wang Bingmei, Zhang Ye, Li Shubin, Hui Qian, Zhang Wenshu. ABNORMAL DATA DETECTION OF NEW DISTRIBUTION NETWORK BASED ON CNN-LSTM HYBRID NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 243-250 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0897

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