Wind power anomaly data detection based on unsupervised methods

Abstract

During the actual operation of the wind turbine, a large number of abnormal data will be generated due to environmental or human factors, which will have a great impact on the condition assessment and output prediction. In order to make wind energy a reliable source of energy, it is very important to establish an efficient and accurate wind power detection model. Therefore, it becomes essential to identify abnormal data for a more precise evaluation of wind turbine performance. Based on the data mining clustering technology K-means algorithm, this paper introduces an unsupervised abnormal wind power detection algorithm combining the variational autoencoders (VAE) model. The focus of this abnormal wind power detection method is primarily on assessing the reconstruction error, which, in turn, generates an abnormality score for wind power data. This score is then used to determine the presence of abnormal wind power data. Finally, the wind power data in 2021 is tested.