Integrated adversarial long short-term memory deep networks for reheater tube temperature forecasting of ultra-supercritical turbo-generators

Abstract

As an important equipment of the ultra-supercritical turbine systems, real-time and accurate control of reheater tube temperature is the key to the normal operation of the system. To improve the accuracy of reheater tube temperature prediction, an integrated adversarial long short-term memory deep network is proposed in this study. Considering the correlation and dynamics between reheater temperature features, different types of single long short-term memory deep networks are batch mixed and stacked in parallel to integrate adversarial training. The training process shares network layer weight parameters to synthesize the decision boundary of each network and complete the dichotomous integration. The proposed networks predict information for the next ten time steps, wirelessly approximate the mapping relationship between multi-source input and output data, and maximize the prediction accuracy and generalization. The proposed networks with single long short-term memory deep networks and 21 algorithms are compared for temperature prediction on the reheater tubes of thermal power plants in Guangdong Province, China, from March 2020 to May 2020. Compared with 90 networks without an integrated adversary, the mean square error obtained by the integrated networks have a 90.85% reduction. Furthermore, compared with the algorithm with the second smallest error among the 22 algorithms, the mean absolute percentage error obtained by the integrated networks is significantly reduced by 99.66%, and the mean absolute error is reduced by 98.36%.