Abnormal Wind Speed Data Recognition Based on Fast R-CNN

With the wide application of wind power generation, the value of wind power generation data is gradually being valued by people. People can look for the key factors that affect the normal operation of wind turbines from massive data, and provide better services for users. In this paper, a method for identifying abnormal wind speed data of wind power generation is proposed. On image data, Faster R-CNN is trained with image data collected daily as samples. The experimental results show that Faster R- CNN can effectively identify abnormal wind speed images. This paper analyzes the influencing factors of the experimental process and results, which provides a reference for the identification of abnormal data in the power system.