Research On Intelligent Optimal Control Of Water Turbine Speed ​​Regulation System

As the core of hydropower station control,the performance of hydraulic turbine governing system will affect the stability of power system and the quality of power.However,there are many uncertain factors in the actual working condition,which make the modeling and precise control of hydraulic turbine governing system difficult.Therefore,the model and control method of hydraulic turbine governing system At present,the research of law is still the focus of research.In this paper,firstly,based on the research status of the turbine speed control system,the structure of each part of the turbine speed control system is analyzed,and a comprehensive model of the turbine speed control system which can meet different working conditions is established,and the model is simulated on MATLAB / Simulink.Due to the different scale of hydropower stations,the requirements of frequency error are different in normal operation,usually the frequency error requirements of large-scale hydropower stations are generally in the range ?2.0HZ,while the frequency error requirements of small-scale hydropower stations are relatively low,which usually only need to be controlled in the range ?5.0HZ.However,due to the great nonlinearity and time-varying of the governing system of hydropower stations,it is difficult for the conventional control algorithm to achieve rapidity Therefore,three intelligent control algorithms are used to optimize the three PID parameters of the controller.The first is to improve the bat algorithm,which gives the original bat algorithm memory function by introducing inertia weight factor?,In the early stage of the algorithm,a large value is given ? to increase the global search ability of the algorithm.In the later stage,when approaching the target,a small value is given ? to slow down the flight speed to enhance the search ability of the local range.The experimental results show that the algorithm can control the overshoot well in response to frequency or load disturbance,Only the regulation time is long,so the controller is suitable for the large-scale hydropower station control with long production line and high requirements for frequency error.The second is the fuzzy neural network control algorithm,which combines the advantages of fuzzy control and neural network algorithm to get the fuzzy neural network algorithm.The results show that the control effect of the controller is obviously better than the traditional PID controller Better,the time needed to adjust to the stable state is shorter than the improved bat algorithm PID controller,but the controller is slightly inferior to the improved bat algorithm PID controllerin terms of control overshoot,but still has good stability,so the controller is more economical for small hydropower stations with short production line and low frequency error requirements;the third is improved phosphorus In this algorithm,evolutionary factors? and optimization operators ? are introduced to the krill swarm algorithm to increase the adaptive adjustment ability of the algorithm.The simulation results show that the PID controller optimized by the algorithm has good stability and regulation performance in response to frequency and load disturbances,and the regulation time is very short without overshoot,It has a good control effect in the experimental process and is suitable for various types of hydropower station control.