Abstract
By upgrading the existing distribution network fault statistical analysis system of Shanghai Institute of Electrical Science and Technology, comprehensive research is needed to investigate the hardware configuration and system functions of the system, optimize the system structure, meet the needs of the company’s distribution network business department, and provide strong support for the daily operation and management of the distribution network. In this paper, we present a semi-supervised learning enabled fault analysis method for power distribution network based on LSTM autoencoder and attention mechanism. We make the LSTM autoencoder’s loss function more robust so that it may be affected by both labeled and unlabeled input. Next, by minimizing the loss function, we can learn how the distribution of both types of data is distributed. We also added an attention mechanism to make the model performance more stable as the weight of the marked data changes. We apply the improved experience of LSTM autoencoder to the LSTM prediction model and realize the LSTM prediction model under semi-supervised learning. The proposed algorithm can effectively solve the problems of strong dependence of time series data and high cost of marking, so as to obtain better fault detection results of power distribution network.
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Xie, H., Zhuang, L., Wang, X. (2023). Semi-supervised Learning Enabled Fault Analysis Method for Power Distribution Network Based on LSTM Autoencoder and Attention Mechanism. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_18
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DOI: https://doi.org/10.1007/978-981-99-3300-6_18
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