In order to realize the abnormal condition detection and analysis of the wind turbine generator

a deep learning method of stacked autoencoder(SAE)network is proposed based on condition monitoring data of supervisory control and data acquisition(SCADA)of wind turbine generator. The SAE network is composed of multiple autoencoder networks

and the normal generator SCADA state variable data is selected as the training input of the network. The distributed rules are extracted intelligently from the network layer by layer to build the SAE learning model. Because internal dynamic balance rules of the generator SCADA data are destroyed under the abnormal state

the reconstruction error is calculated using the initial input and reconstruction values as the observation of the whole state. Adaptive threshold is used to detect the trend of the reconstruction error and as a criterion for abnormal early warning to detect generator abnormal condition. When the generator is abnormal

there is a larger deviation between the actual value and its reconstruction value. So the residuals trend of the state variables will deviate from the original dynamic stability state. The trend change of the state variable residual will be used to isolate the abnormal variables to locate the generator fault and achieve the purpose of fault diagnosis. The simulation results show that the SAE network deep learning method is effective for condition monitoring and fault diagnosis of generator.