Employment of Kernel Methods on Wind Turbine Power Performance Assessment

Abstract

A power performance assessment technique is developed for the detection of power production discrepancies in wind turbines. The method employs a widely used nonparametric pattern recognition technique, the kernel methods. The evaluation is based on the trending of an extracted feature from the kernel matrix, called similarity index, which is introduced by the authors for the first time. The operation of the turbine and consequently the computation of the similarity indexes is classified into five power bins offering better resolution and thus more consistent root cause analysis. The accurate and proper detection of power production changes is demonstrated in cases of icing, power derating, operation under noise reduction mode, and incorrect controller input signal. Finally, overviews are illustrated for parks subjected to icing and operating under limited rotational speed. The comparison between multiple adjacent turbines contributes further to the correct evaluation of the park overall performance.