Adaptive Temperature Prediction Method for Wind Farm Equipment Based on Generalized Regression Neural Network
Traditional prediction methods are difficult to effectively handle the nonlinear relationship between various influencing factors of wind farm equipment temperature,resulting in inaccurate prediction results.To address the above issues,a temperature adaptive prediction method for wind farm equipment based on generalized regression neu-ral network is studied.Analyze the factors affecting the temperature of wind farm equipment and collect data corre-sponding to these factors to form a sample,and perform outlier and normalization processing on the sample.Using PIO algorithm to adaptively adjust the parameters of the generalized regression neural network prediction model-smoothing factor σ,improve its adaptive ability.The results indicate that the prediction bias of the studied method is small,the maximum error is only 0.3℃,indicating that the method has higher accuracy in predicting temperature and the predicted values are closer to the actual values.
generalized regression neural networkwind power plantsequipment temperaturePIO algorithmadaptive prediction method
万方数据
维普
