A hybrid model based on advanced optimization algorithm, and deep learning model for wind turbine sensor condition monitoring and fault identification

Sensors are crucial components of wind turbines, and their stable and reliable operation directly affects the safety and economic benefits of wind turbines. To effectively monitor the status of the sensors, this paper proposes a technique for monitoring the status and fault identification of wind turbine sensors based on multi-strategy optimization Harris Hawks optimization (MHHO) and deep belief network (DBN). Firstly, the input and output parameters of wind speed sensor and temperature sensor are selected using the mixed correlation index. Second, the MHHO-DBN-based wind turbine sensor state monitoring and fault identification model is established, the time sliding window performance evaluation index is constructed, and the threshold of the wind turbine sensor abnormality index is determined according to the interval estimation theory of statistics. Then, a mathematical model is established to identify the faults of sensors with abnormal states. Finally, the MHHO-DBN model is established to monitor the actual sensor state, and the mathematical model is used to identify the fault. The calculation results reveal that this technique can effectively monitor the state of the wind turbine sensors and recognize the sensor fault categories in time, which is of good engineering practical meaning for improving the safety of wind turbine operation.