基于卷积孪生网络的风力机状态监测研究

The operation faults of wind turbines are sudden and there is almost no reserved time for people to respond. In turn, some minor defects will cause a series of chain faults and unnecessary losses. Therefore, monitoring the conditions and predicting the conditions in advance are necessary. In this article, based on convolutional siamese networks, SCADA data of wind turbines are combined for model training-based condition monitoring. Historical healthy data is used for offline model training, and the pre trained models are loaded into the online monitoring system after testing. Status-indication is defined to describe the operating conditions of the wind turbine. The monitoring threshold of abnormal wind turbines is proposed according to statistical process control during online monitoring. With the analysis and comparison of SCADA data and fault reports of a wind farm in Gansu Province, the method proposed in this paper is in good performance in monitoring and predicting wind turbine conditions.