Switchgear Circuit Breaker Contact Temperature Prediction Method Based on LSTM-PLS

High-voltage switchgear contact overheating is a critical factor in power system failures, requiring effective prediction models for safe operation and maintenance. This paper proposes a two-step prediction method based on Long Short-Term Memory (LSTM) networks and Partial Least Squares (PLS) regression to address medium- and long-term prediction challenges. The LSTM model predicts future ambient temperature and load current using historical data from a 10kV substation in Fujian Province, China, collected every 5 minutes, with trigonometric features incorporated for periodicity. PLS regression then uses these predictions to estimate switchgear contact temperature. The dataset comprises 2018 data points spanning seven days. Experimental results demonstrate that the proposed LSTM-PLS model achieves a 37.3% reduction in RMSE and an 8.4 % improvement in correlation coefficient compared to standalone LSTM, with a 40.8% reduction in error against other combined models. This method significantly enhances accuracy and efficiency, offering a practical solution for monitoring heating in high-voltage switchgear under real-world conditions. Future research could explore integrating additional features, such as environmental factors or advanced optimization algorithms, to further refine the prediction performance.