|
The structure of wind turbine is complicated and the operating environment is harsh, which leads to the high probability of failure of wind turbine, and the maintenance is more difficult. Once an accident occurs, it will force the wind turbine to shut down, causing huge economic losses. In addition, it will affect the safe and reliable operation of the whole power system. Therefore, fault diagnosis of wind power generation system is very important. The traditional fault diagnosis algorithm can only identify the known types of faults in wind turbines. If a new fault category appears in the wind turbine generator, the traditional fault diagnosis algorithm can only identify it as a known fault category. To solve this problem, a new classification fault diagnosis method based on semi-supervised deep learning is proposed. The benchmark model of offshore wind turbine is built in FAST software, and 10 different types of faults such as sensors and systems are simulated through model simulation. Different responses of fault signals are analyzed, and 15 parameter signals are selected as input. Firstly, fault features are extracted through multi-scale convolutional self-coding network. Secondly, the initial model is established by using the compressed feature and the error feature map as the input of the classifier and the detector respectively. Finally, the detector judges and puts the new category of fault instances into the buffer. When the buffer reaches the maximum value and starts to overflow, the algorithm starts to update, so as to realize the diagnosis task of new types of faults. Compared with the experimental results of isolated forest, local anomaly factor and single-class support vector machine, the accuracy of our proposed method and other indicators are significantly better, with an accuracy of 99.2%, which can effectively solve the problem of new class fault identification of wind turbines. It can effectively solve the problem of fault identification of the new type of fan, and is conducive to avoiding the occurrence of shutdown accidents, thus maintaining the safe and reliable operation of the power system.
|
