Research Of Hydro Turbine Governor Control Strategy And Identification Method

The hydroelectric generating system of water power plant is a high-order, non-linear, with time-variable and non-minimal phase properties system. The reliable control of hydroelectric generating system is a key of water power plant safe running. In order to research better intelligent hydro turbine governor to improve control performance, the dissertation summarizes the existing control strategies and identifying methods. Combining the character of complicated hydroelectric generating, intelligent control strategies and identifying methods have been researched and some concrete measures of improvement and designing methods of intelligent governor are put forward. The corresponding application simulating results are given. After analyzing general math modeling and conventionality artificial neural network modeling of hydroelectric generating, the ANFIS network based on Takagi-Sugeno is put forward to identify the character of hydroelectric generating. To settle its slow convergence problem in turning network parameters when error is less,analogy Newton algorithm and gradient-descent mix algorithm are adopted to turning network parameters , which bright excellent real-time character and provided a brand new way to the identification of hydroelectric generating.According to the problem existing in the regular PID control, some optimizing methods of PID control are further analyzed and their relevant design and simulating results are given. Then the advantages and disadvantages are discussed based on simulating experiment results, which provides convincing proves for better choice PID optimizing methods. Based on all above work, mix control strategy based on PID control with fuzzy optimizing and fuzzy control is put forward. The simulating results prove its validity.To improve the controllability of bigger water power hydroelectric generating system,a new hydroelectric generating controller is designed based on fuzzy neural network. The structure, model and study approaches are introduced in detail in the dissertation as well. To handle the problem that slow convergence speed or shake brought by using back-propagation method only in learning,the self-adapt learning method is used to turning parameters. To ensure the stability and convergence of fuzzy neural network controller during the learning process,network parameters are chosen based on Lyapunov theory. The simulating results proved its validity.As for choosing parameters of the fuzzy neural network controller having some subjectivity and probing, soft-computing method based on fuzzy reference system, neural network and genetic algorithm is put forward to control hydroelectric generating. The structure and design of controllers are given. The improved optimizing method of genetic algorithm is put forward to solve the problem that existed in the optimizing of genetic algorithm. Control monitor is used to avoid brought the problem bad performance or shake when fuzzy neural network optimizing in bigger work condition change. The simulating results of designed intelligent controller are given.Finally, the dissertation summarizes all the works and results achieved in this dissertation. The further research works to be developed are also put forward.