Large wind turbine unit individual pitch control method based on RBF neural network

Abstract

The invention discloses a large wind turbine unit individual pitch control method based on an RBF neural network. The method includes the following steps that wind wheel rotating speed signals are collected so that a unified pitch angle and electromagnetic torque can be obtained; three paddle root bending moments and paddle azimuth angles of a wind turbine unit are calculated; the three paddle root bending moments are subjected to Coleman coordinate transformation, so that a pitching bending moment and a yawing bending moment are obtained; the self-adaption rate of the neural network is derived through self-adaptive control of the RBF neutral network, the weight of the neutral network is adjusted in an on-line mode so as to improve the paddle root bending moment of an individual pitch control system, and then the weight is converted into the optimized pitch angles of different paddles through Coleman inverse transformation; and the unified pitch angle plus the optimized pitch angles obtains an individual pitch control pitch angle, and the optimized pitch angles are fed into a variable pitch execution unit so that individual pitch control can be completed. According to the large wind turbine unit individual pitch control method based on the RBF neural network, individual pitch control can be realized quickly, so that the working efficiency of a variable pitch servo system is improved, controlling cost is low, and the service life of the wind turbine unit is prolonged.

Description

technical field [0001] The invention relates to the field of large-scale wind turbine control, in particular to an RBF neural network-based independent pitch control method for large-scale wind turbines. Background technique [0002] In recent years, the depletion of natural resources, coupled with the increasingly severe environmental impact, the energy crisis has emerged. No matter from the perspective of technology maturity, market size, or price and cost, wind power generation is currently one of the most promising new energy technologies. Wind turbines with pitch control have become the main research direction of large wind turbines due to their advantages such as high wind energy utilization coefficient, flexible structure, and wide wind speed operation area. [0003] The simple and reliable conventional PID control method is widely used in the pitch control of wind turbines, but the parameters of PID control are constant, and there is great uncertainty for the comple...

AI Technical Summary

Problems solved by technology

Many researchers have performed optimal control by adding first-order transmission chain damping to the pitch system, but the realization of the optimal control method is based on the establishment of an accurate mathematical model, and the actual wind turbine pitch The system is a complex time-varying nonlinear system, and it is difficult to establish an accurate control system model

Some researchers have also analyzed the dynamic load of the wind turbine, and proposed a multi-degree-of-freedom independent pitch control system on this basis, and established a multi-degree-of-freedom linear model to complete the independent pitch control of the wind turbine, but there is no sufficient Considering the nonlinear coupling dependencies of the wind power transmission system

There are also researchers who analyze the correlation between the multi-input and multi-output variables of the wind turbine pitch system, and use the linear quadratic Gaussian function to estimate the fan state feedback to the controller to design a multi-variable optimal independent pitch controller, and through The simulation proves the good performance of its control strategy, but it does not consider the influence of unbalanced loads on wind turbines due to wind shear, tower shadow effect and turbulence

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