Comparison of two state evaluation methods for wind power generation equipment

Abstract

The existing intelligent warning system achieved intelligent warning through state prediction, state evaluation, and fault localization. This article proposed and compared two state evaluation methods based on adjustable smoothing parameters and sliding window similarity. Actual wind turbine data was used as training data for the multivariate state prediction model, and the predicted data was used as input for the two state judgment methods. It was concluded that the sliding window similarity method has advantages in early warning accuracy, timeliness, and simplicity. Finally, the influence of sliding window parameters on early warning sensitivity was analyzed and discussed.