Research On Control Algorithm Of Hydraulic Turbine Regulating System Considering Nonlinear Characteristics

At present,the strong click capacity and complicated water diversion system of hydropower stations built in our country make the modeling of hydraulic turbine regulating system(HTRS)extremely difficult,which brings severe tests to the safe and stable operation of the power system.Based on the working principle of the HTRS,this paper sets up the mathematical models of HTRS under linear and nonlinear conditions,and condenses the problems faced by HTRS in the design of control laws,parameter optimization of PID control algorithms and sliding mode control algorithms.Combined with the artificial intelligence algorithm to optimize the parameters of different control algorithms,so as to achieve the improvement and optimization of HTRS performance.Firstly,the working principle and composition structure of HTRS are analyzed,and the corresponding mathematical model expressions are established for each part by the method of block modeling.Different mathematical models are established for different fluctuations,corresponding to the linear HTRS mathematical model in the case of small fluctuations and the nonlinear HTRS mathematical model in the case of large fluctuations,respectively.Next,the simple and easy-to-operate PID control algorithm is used to optimize the dynamic characteristics of linear HTRS system under small fluctuations.For the nonlinear HTRS mathematical model under large fluctuations,the sliding mode variable structure control(SMC)algorithm with nonlinear characteristics is used to analyze the dynamic performance of the entire system.Among them,for the shortcoming that the traditional SMC control algorithm is prone to chattering,an improved PID sliding mode control algorithm(IPID-SMC)is proposed,which integrates the PID sliding mode surface.An artificial bee colony(ABC)algorithm is selected to optimize the required parameters of the PID,SMC and IPID-SMC control algorithms that used above.Then the improved algorithms are proposed,which are resilient toward local optimization and premature convergence in standard ABC.On the one hand,the global optimization mechanism in particle swarm optimization(PSO)algorithm are added for enhancing the global optimization ability of the bee colony.On the other hand,the position adjustment percentage is introduced and optimized by fuzzy control algorithm,a modified fuzzy artificial bee colony(MFABC)algorithm is showed to balance the local mining ability and global optimization ability of the bee colony.In order to verify the function optimization ability of the MFABC algorithm,a number of benchmark test functions are used for simulation experiments.The experimental results show that the proposed MFABC algorithm has advantages in convergence and stability compared with several other intelligent algorithms.And the dynamic performance of the HTRS corresponding to the PID,SMC and IPID-SMC control algorithms optimized by the MFABC algorithm has been improved.