Open AccessWind Turbine Power Curve Modelling Based on Hybrid Relevance Vector Machine
Author(s) -
Bo Jing,
Qian Zheng,
Anqi Wang,
Tianyang Chen,
Fanghong Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1659/1/012034
Subject(s) - relevance vector machine , wind power , turbine , computer science , range (aeronautics) , computation , power (physics) , limit (mathematics) , relevance (law) , control theory (sociology) , support vector machine , artificial intelligence , algorithm , mathematics , engineering , control (management) , mechanical engineering , mathematical analysis , physics , quantum mechanics , aerospace engineering , law , political science , electrical engineering
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.