A power performance online assessment method of a wind turbine based on the probabilistic area metric

Abstract

This paper presents an approach for creating online assessment power curves by calculating the variations between the baseline and actual power curves. The actual power curve is divided into two regions based on the operation rules of a wind turbine, and the regions are individually assessed. The raw data are filtered using the control command, and outliers are detected using the density-based spatial clustering of applications with noise clustering method. The probabilistic area metric is applied to quantify the variations of the two power curves in the two regions. Based on this result, the variation in the power curves can be calculated, and the results can be used to dynamically evaluate the power performance of a wind turbine. The proposed method is verified against the derivation of secondary principal component method and traditional statistical methods. The potential applications of the proposed method in wind turbine maintenance activities are discussed.