PREDICTION AND UPDATE OF MAIN SHAFT FORCE OF WIND TURBINE BASED ON MECHANISM AND DATA FUSION

To monitor the force state of the main shaft of wind turbine in real time, the paper constructs an adaptive update prediction model that integrates mechanism and data. Firstly, adopting a “physical-baseline plus deviation-compensation” strategy, the finite element mechanism model is used to provide the prediction reference value, and the data-driven model based on the measured data is combined to compensate for the deviation, thereby overcoming the limitations of a single model. Secondly, an incremental learning method with a variable forgetting factor is implemented. This mechanism enables online learning of new data to update the network parameters, thereby alleviating the performance degradation problem during long-term operation. Validation results demonstrate that the prediction error of the main shaft internal force is stable within 3%, and the update mechanism ensures the long-term prediction accuracy.