ASL-CatBoost Method for Wind Turbine Fault Detection Integrated with Digital Twin

In view of the low visibility of the current wind farm status monitoring and insufficient real-time operation and maintenance, based on the concept of digital twin five-dimensional model, the framework of wind farm digital twin five-dimensional model is constructed. Aiming at the insufficient fault detection capability of traditional algorithms and unbalanced positive and negative samples in fan fault data set, the improved ASL-CatBoost algorithm is proposed to achieve the accurate detection of fan fault status. Based on the digital twinning platform, combined with MATLAB/Simulink, the simulation mathematical model of doubly-fed wind turbine under the condition of blade mass imbalance is established, and the auxiliary fault detection algorithm is used to simulate the data of wind turbine fault conditions caused by different reasons under various wind speeds and temperatures to train and enhance the generalization ability of the algorithm. A case of the integrated platform of wind farm digital twinning operation, maintenance and control is designed. Driven by real-time data, the real-time monitoring and accurate control of wind turbine operation status is realized, and the feasibility of the proposed framework and improved algorithm is verified.