Multi-feature-scale fusion temporal convolution networks for metal temperature forecasting of ultra-supercritical coal-fired power plant reheater tubes

The ultra-supercritical coal-fired power plant has higher reheater steam temperatures. To reduce the possibility of overheating operation and decrease the explosion of reheater, the operating temperature of reheater should be accurately monitored in real-time. Through the real-time prediction of the temperature distribution of the reheater tube, the alarm threshold is set to prevent tube burst and other faults. To accurately predict the metal temperature of the reheater tube in real-time, a multi-feature-scale fusion temporal convolution network prediction model is proposed in this paper. The model has a strong ability of feature extraction and nonlinear function fitting, and can infinitely approximate the mapping relationship between input and output data. Innovative methods feature construction, spatial partition and feature fusion, are proposed to improve the accuracy. Original data are collected from one thermal power plant reheater in the Guangdong province of China. The multi-feature-scale fusion temporal convolution network is applied to study the real-life data from March 2020 to May 2020. Compared with 20 prediction models such as random forest, the proposed model has higher accuracy. The mean absolute percentage error result of the proposed model is smaller 0.16% than the second smallest model and 25.70% smaller than the largest model. The operating temperature of reheater tubes is accurately monitored in real-time.A multi-feature-scale fusion temporal convolution network model is proposed.The model has a strong ability to feature extraction and nonlinear function fitting.The method can fully consider the time delay characteristics of reheater tubes.The metal temperature of reheater tubes is accurately predicted in real-time.