Identification And Parameter Optimization Of Turbine Governing System

With the constant change of energy structure,people's demand is increasing,and hydropower resources are getting more and more attention.Hydro.turbine governing system is an important part of the hydro.generator set.Its performance has a direct impact on the safety and stability of the power system.Therefore,improving the performance of the system and mastering its operation law are important links in the study of the hydro.turbine governing system.Therefore,through the parameter identification research and PID parameter tuning of the hydraulic turbine speed control system,it plays a vital role in improving the efficient and stable operation of the hydraulic turbine speed control system.The main work and innovation of this paper are as follows:(1)The governing system of hydraulic turbine is divided into five parts: governor,hydraulic servo system,water diversion system,hydraulic turbine and generator and their loads.By analyzing the principles of each part,the mathematical models and corresponding mathematical expressions of each part are deduced.The overall mathematical model of the governing system of hydraulic turbine is built and combined in simulink.(2)By analyzing the system identification,the pseudo-random binary sequence is selected as the excitation signal of the actual system and the model of the hydraulic turbine governing system,and the output of the excitation signal and the actual system is obtained by combining the MATLAB algorithm with the parameters of the hydraulic turbine governing system.(3)Because of the premature convergence of particle swarm optimization algorithm,the performance of the system can not be well obtained in the parameter identification of hydro.turbine governing system.In this paper,the PSO algorithm is optimized by changing the learning factor,combining the gravitation algorithm with the PSO algorithm,and introducing the dynamic learning factor into the PSO.gravitation algorithm.The results of parameter identification of hydraulic turbine governing system show that the application of dynamic learning factor in particle swarm optimization.gravity algorithm has better optimization ability.(4)In view of the fact that the traditional PID controller can not adjust well when the turbine speed control system is in working condition,the quantum genetic algorithm and particle swarm optimization are combined.To solve the problem that the algorithm may fall into local optimum when the quantum gate rotates at a certain angle,a global optimum position and growth rate are introduced to reflect the chromosome.Evolution.Finally,the improved algorithm and particle swarm optimization.gravity algorithm are applied to the PID control of hydraulic turbine governing system respectively,and a certain load is addedafter the system is stabilized to compare.The results show that the quantum genetic particle swarm optimization algorithm is more feasible and effective.In summary,the advantages and disadvantages of particle swarm optimization are improved.Finally,it is concluded that quantum genetic particle swarm optimization is feasible to improve the stability of hydraulic turbine governing system.