Health performance evaluation and prediction methods for wind turbines based on XGBoost-Bin automatic power limit calculation

The health performance evaluation areas of the wind turbines are difficult to divide thanks to the large data, which influences the health performance prediction accuracy. To tackle these problems, an automatic power curve limit calculation algorithm based on XGBoost-Bin was proposed, and a health performance prediction model considering multiple data characteristics was developed. A static optimal power curve construction algorithm based on XGBoost-Bin was proposed to reflect the operating states of the wind turbines. An improved automatic power curve limit calculation was proposed to accurately divide the wind turbine health performance evaluation areas, and evaluate the health performance by analyzing the deviation of the actual and theoretical static optimal power curves and power generation efficiency. A wind turbine health performance prediction model based on multi-data features Generalized Regression Neural Network (GRNN) was proposed to improve the accuracy of wind turbine health performance prediction accuracy. 20 wind turbines of Saihanba wind farm in Inner Mongolia of China were taken as an example to be analyzed. The experimental results showed that compared with the traditional automatic power curve limit calculation methods, the evaluation accuracy of the proposed health performance evaluation method for wind turbines was improved by 0.1026. The proposed prediction model could enhance the prediction accuracy of the health performance of wind turbines. Compared with the traditional random forest prediction model, the indicator of R2 with the proposed prediction model was enhanced by 0.017. © 2024 CIMS. All rights reserved.