Research On Parameter Identification And Control Method Of Excitation System For Hydro-Generator

Hydropower has a promising future as a sustainable energy source.However,as the capacity of hydropower plants increases,the stability of the power grid is affected by the safe and stable operation of hydropower plants.One of the key factors to ensure the stable operation of hydropower plants is the hydroelectric generator runs stably.As an important part of the hydro-generator,the excitation control system will directly affect the stable operation of the generator.In order to improve the steady-state performance of the hydro-generator and reduce the impact of the hydropower plant on the stable operation of the power grid.In this paper,the excitation control system is researched,the parameter identification and control strategy of the excitation control system.In this paper,the transfer function of each unit module of the excitation control system is deduced first,and the mathematical model of the excitation control system is constructed,which lays a foundation for the parameter identification and control strategy research of the excitation system.In order to establish a precise mathematical model of the excitation control system,some parameters in the model are parameterized.In order to avoid the disadvantages of gravitational search algorithm,such as memorylessness,an improved gravitational search algorithm is proposed by combining gravitational search algorithm with particle swarm optimization algorithm.At the same time,gravitational search and improved gravitational search algorithm are used in the parameter identification of excitation control system respectively.The results show that the improved gravitational search algorithm has better ability to search.In order to improve the excitation control system to obtain better control performance,at the same time,to explore the application of intelligent control method in the system PI parameter tuning.Firstly,this paper detailly introduces the theoretical knowledge of basic quantum genetic algorithm,then improves some shortcomings of quantum genetic algorithm,and uses genetic algorithm,basic genetic algorithm,quantum genetic algorithm and improved quantum genetic algorithm to optimize PI parameters of excitation control system.The results show that the improved quantum genetic algorithm has higher robustness,and the time-consuming of the algorithm is shorter than that of the basic quantum genetic algorithm.Finally,the average of the PI algorithm optimization results obtained by the three algorithms is substituted into the excitation control system,and the step response of the system is observed.It can be seen that the improved quantum genetic algorithm has better control performance.When the excitation control system is faced with complex signal input,its PI parameters can be adjusted online,so that the system remains stable.The adaptive control of BP neural network based on IQGA is studied.Firstly,the PI parameter is adjusted by the IQGA to obtain the initial PI parameter value of the control system.Then the BP neural network is used to optimize the increment of the output PI parameter change.The initial value of the parameter is added to the incremental increment of the PI parameter to obtain the true PI parameter of the excitation control system,thereby realizing the purpose of setting the PI parameter online according to different input signals of the system,so that the system maintains good performance operation.