The discriminant model of wind turbine SCADA normal data based on random forest

Abstract

Nowadays, the number of assembled wind turbines in the world is growing more rapidly, which brings an urgent need for intelligent operation and maintenance of wind turbines. The intelligence of wind turbine operation and maintenance is based on the high-precision classification and recognition of SCADA system data. In response to this demand, this paper establishes a wind turbine normal data discrimination model that combines SCADA system data preprocessing and random forest integrated learner. First, obtain a determinable sample dataset according to the principles of statistics and the NearMiss under-processing method. Then build a decision tree, use the features in a variety of SCADA datasets to train and learn the sample dataset, and form a random forest to determine the normal data model of wind turbines. The results show that the model can effectively classify whether the SCADA data of wind turbines is normal, achieve a higher accuracy rate, and improve the reliability of discrimination, which is of great significance to the subsequent research on intelligent operation and maintenance of wind turbines.