Abstract.A new data-driven condition monitoring method for wind turbines is proposed to prevent a turbine failure in a wind farm. The method works through the power curve model which is built with historical SCADA data. New constraints that established based on Betz' law and RC model are developed for governing the power curve model. Since abnormal data has a strong impact on the power curve model, the Inner-DBSCAN algorithm is proposed to reject it. In addition, we use an edge recognition method for normal data to form the power curve model. Then, the turbine operation condition can be monitored through the model. Its effectiveness through industrial studies is confirmed, and the time cost of building the power curve model is only 1.08s.
Abstract. In order to solve the problem that the identification method of wind turbine is not suitable for large data environment. In this paper, we propose FSIQUE. Firstly, the data space is divided into dense grid cells and sparse grid cells. Based on the FS-Tree storage structure proposed in this paper, a dense grid cell is stored and the subspace is traversed by this storage structure. Secondly, traversing the connected grid cells in the subspace to find the clusters. Finally, the maximum and minimum coverage of the cluster is calculated. According to the parameter, the clustering is divided into normal data and abnormal data to realize the anomaly recognition. The proposed method is run on the Spark platform and compared with the WPMCLU and DBSCAN methods, has the highest abnormal recognition rate and the runtime is shortest.
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