Process Control Optimization for Hydroelectric Power Based on Neural Network Algorithm

It is crucial for a hydropower station to operate smoothly in a safety and steady way. In this paper, we explore the current development trend and intelligent control theory of hydropower control system. The mathematical expressions are set up here for the system hardware and software, respectively. An integrated model was also developed for servo system of each mathematical model linear combination, which lays a theoretical foundation for model identification of hydropower units; this paper introduces the neural network and the fuzzy control theories and analyzes the BP and RBF network structures and identification simulation. The experimental results show that the RBF network can improve the iterative efficiency, speed up the analysis and identification, and avoid sluggish condition of global approximation convergence. The fuzzy control rule for PID controller is optimized as a result the Fuzzy PID control may speed up the adjustment, and increase the speed at which the system tends to be stable, provided that the system stability is ensured. It, therefore, contributes the most to the safety of the power grid.