Substation Equipment Temperature Prediction Method considering Local Spatiotemporal Relationship

Abstract

Temperature prediction of substation equipment is one of the important means for intelligent inspection of substation equipment. However, there are still three challenges: (1) Limited extracted samples; (2) Typical nonlinearity, seasonality, and periodicity; (3) Changes in equipment and working conditions. To solve the problems above, a substation equipment temperature prediction method considering Spatio-temporal relationship (SETPM-CLSTR) is proposed. First, according to the time series of equipment temperature from two aspects of temporal and spatial, it is determined that the equipment temperature has seasonal, temporal, and spatial correlation; second, aiming at the problem that the spatial location correlation cannot be described quantitatively, grey relational analysis (GRA) is adopted to determine the spatial location monitoring points closely related to the prediction target; then, the daily maximum temperature and daily minimum temperature from the environment, the predicted target temperature from the past several times in time and the temperature from the spatial location monitoring point with close correlation in space are constructed as Spatio-temporal feature vectors; finally, CNN-BiLSTM double-layer depth network model is proposed to predict the equipment temperature. SETPM-CLSTR has applied to temperature prediction of phase A contact from primary equipment of a substation in Taizhou City, Zhejiang Province. Under the two prediction performance evaluation indexes of MASE and RMSE, compared with three correlation models of LSTM, BiLSTM, and CNN-LSTM from two aspects of different features and models, it is verified that SETPM-CLSTR in this study has better prediction performance.

Strip thickness prediction method based on improved border collie optimizing LSTM

“…In the experiments, the training data set contains 800 training samples, and the test data set contains 100 test samples. The root mean square error (RMSE) is adopted as an evaluation index to evaluate the prediction accuracy of the six models ( Sun et al, 2022 ). Each model is repeated five times to obtain the average RMSE.…”

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

“…The research results are mainly concentrated in domestic Huazhong University of Science and Technology, Harbin University of technology, Zhejiang University, North China Electric Power University and some power companies ( Hao et al, 2021 ; Guo et al, 2020 ; Kong, 2015 ). The research objects of substation equipment at home and abroad mainly include high-voltage or low-voltage switchgear ( Velásquez, Lara & Melgar, 2019 ; Zeng et al, 2018 ; Bussière et al, 2017 ), intelligent electronic equipment ( Sun et al, 2022 ), disconnector ( Huang et al, 2022a ), bushing contact ( Huang et al, 2022b ), etc . At present, most studies used historical time series as feature extraction source for rolling prediction of equipment temperature, which typically include auto-regressive and moving average model (ARMA) series models (AR, ARMA, ARIMA) ( Baptista et al, 2018 ); however, the simple temperature trend can not accurately predict the future equipment temperature value, resulting in the failure to accurately identify the health status of the equipment and take precautions in advance.…”