Abstract
A fault early warning method based on genetic algorithm to optimize the BP neural network for the wind turbine pitch system is proposed. According to the parameters monitored by SCADA system, using correlation analysis to screen out the parameters of the pitch system with strong power correlation. The BP neural network optimized by genetic algorithm is used to establish the model of the pitch system under normal working conditions. The verification results show that the input parameters of the pitch system model determined by the correlation coefficient are more reasonable, and the accuracy of the pitch system model established by the genetic algorithm-optimized BP neural network is higher than that of the unoptimized model. Based on the above model, a sliding window model is established, and the early warning threshold is determined through the statistics of the residuals of the sliding window to realize the fault early warning of the pitch system of the wind turbine. The example shows that the method can give early warning in the event of failure, and verifies the effectiveness of the method.
Highlights
With the development of the wind power industry in recent years, the installed capacity of wind turbines in China has increased year by year
It is fully proved that the prediction effect of genetic algorithm optimized BP (GA-BP) neural network is better
The GA-BP neural network model is selected as the fault early warning model of the wind turbine's pitch system
Summary
With the development of the wind power industry in recent years, the installed capacity of wind turbines in China has increased year by year. The pitch system is one of the important control systems It includes pitch motor, pitch control cabinet, pitch driver, etc. Reference [1] established a neural network model of gearbox bearing temperature based on principal component analysis to realize early warning of gearbox failure. Reference [2] used a weighted principal component analysis method to establish a generator temperature model under normal operating conditions, and accurately analyzed generator temperature faults by analyzing statistics and squared prediction error trends. Reference [3] based on nonlinear state estimation of the wind turbine pitch control system model can diagnose major and minor faults. The fault early warning threshold is determined based on the sliding window model, and the fault early warning of the pitch system is realized
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