A Optimal Method of Turbine Adjustment System Control Parameters

Abstract

The present invention discloses a preferred method of a water turbine adjustment system control parameter to carry out preferred selection of a PID control parameter in a water turbine adjustment system. A model is established according to the water turbine adjustment system, then a target function with a PID controller control parameter as a preferred variation is established according to a simulation system, and the preferred method designed by the invention is used to solve the target function to obtain an optimal PID control parameter. According to the preferred method of the water turbine adjustment system control parameter, a novel heuristic optimization algorithm is used to optimize the target function, a better target function value can be searched, and an obtained solution represents a better PID control parameter. According to the better PID control parameter, the frequency deviation of the water turbine adjustment system is small, the adjustment speed is fast, a system response curve is smooth, and the system adjustment quality is high.

Description

technical field [0001] The invention belongs to the technical field of hydroelectric power generation, and more specifically relates to a method for optimizing control parameters of a water turbine regulating system, which is used for optimizing the control parameters of a controller in the water turbine regulating system. Background technique [0002] The hydraulic turbine regulating system is the core control system of the hydroelectric unit, which is responsible for stabilizing the frequency of the unit and regulating the power of the unit. In engineering applications, the control law adopted by this system is generally proportional-integral-derivative controller (Proportion Integration Differentiation, PID) control. PID control parameters have a decisive impact on the adjustment quality and dynamic response indicators of the unit, how to optimize the PID parameters has become a difficult problem in engineering applications. In actual operation, PID control parameters gen...

AI Technical Summary

Problems solved by technology

The gradient method requires the objective function to be continuously differentiable; the simplex method is greatly affected by the initial value and the calculation step size, and it is easy to converge to a local optimal solution; the genetic algorithm needs to perform replication, crossover and mutation operations, the evolution speed is slow, and it is easy to produce premature Convergence, and its performance has a large dependence on parameters; PSO algorithm has shortcomings such as premature maturity and local minima in solving complex optimization problems

These defects may cause the algorithm to fail to obtain the optimal control parameters of the turbine regulation system.

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