Affiliations: [a] School of Electrical Engineering, Changchun Institute of Technology, Changchun, Jilin, China | [b] Baishan Power Supply Company, State Grid Jilin Electric Power Company Limited, Jilin, China | [c] Department of Electrical Engineering, Texas A&M University, USA
Correspondence:
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Corresponding author: Hongbin Sun, School of Electrical Engineering. Changchun Institute of Technology, Changchun, Jilin, China. E-mail: [email protected].
Abstract: Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.
Keywords: LSTM, deep learning, transmission line network, mesh, failure prediction
