Wind power variable-pitch multi-variable fuzzy neural network PID control method

Abstract

The invention relates to a wind power variable-pitch multi-variable fuzzy neural network PID control method. The control method includes the following steps that a fuzzy parameter setting module is used for presetting the weight of a PID neural network module; the error between a rotating speed reference value and the actual rotating speed output of a wind driven generator is calculated through a PID calculation module to obtain a reference output quantity of torque of the wind driven generator; the error between the power output value and the power reference value of the wind driven generator and the error change rate are set through the fuzzy parameter setting module to obtain a presetting parameter of the weight of the PID neural network module; through a negative gradient algorithm with a momentum factor, the weight of the PID neural network module is trained, and the reference value output of torque and the reference value output of the pitch angle of the wind driven generator are adjusted. The output power of the wind driven generator can be stabilized nearby a rated valve, and safety of a fan is ensured.

Description

technical field [0001] The present invention relates to a wind power variable pitch multivariable fuzzy neural network PID control method, in particular to a wind power variable pitch multivariable fuzzy neural network PID control method, which is applicable to the pitch control method of double-fed wind turbines and belongs to The technical field of wind power control. Background technique [0002] Wind energy is a green and renewable energy, and its proportion in green energy is increasing year by year. The development and utilization of wind energy has broad commercial prospects. When the wind speed is higher than the rated value, how to effectively control the pitch system to reduce the power fluctuation and mechanical fatigue of wind turbines has attracted extensive attention. The more commonly used methods include PI control and LQG control. PI control adjusts the pitch angle through the error between the actual speed value of the wind turbine and the speed reference...

AI Technical Summary

Problems solved by technology

PI control adjusts the pitch angle through the error between the actual value of the wind turbine speed and the reference value of the speed. However, the PI control method requires offline training of a large number of parameters, which greatly reduces the control accuracy of the system.

LQG control is difficult to ensure the global asymptotic stability of the system, and it increases the calculation amount of the control system

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