Wind Turbine Power Curve Modelling Based on Hybrid Relevance Vector Machine

Abstract

Wind turbine power curve (WTPC) is important for energy assessment, condition monitoring and abnormal detection. In recent years, researchers proposed a number of WTPC modelling approaches to continuously improve the model performance. In this paper, Relevance Vector Machine (RVM) is applied for WTPC modelling for the first time. Combine single-input RVM and multi-input RVM, this paper proposes a hybrid RVM method (HRVM) to further improve the fitting accuracy. Firstly, we analyse the features of model outputs of both single-input RVM and multi-input RVM. According to the analysis, the confidence interval of single-input RVM is used to limit the power output range of multi-input RVM. At last, SCADA data collected from three wind turbines are used to test the model performance. The results show that, compared with typical WTPC model approaches, HRVM achieves a good balance between fitting accuracy and computation cost.