Minimum Risk Quantification Method for Equivalent Error Threshold of Wind Farm Based on Bayes Criterion

Abstract

The equivalent error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) model, and can promote the standardization process of WF equivalent model. Major wind power countries in the world have different starting points and emphases in quantifying the error threshold of wind power models, and the form and indicators of the error threshold have not been unified. Therefore, this paper puts forward a method based on Bayes criterion to quantify the minimum risk of equivalent error threshold of WFs. Firstly, taking the time distribution characteristics of equivalent errors as the starting point, the Euclidean errors of equivalent models of WFs in different periods are quantified, and then the probability density distributions of the above errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the effective prior probability of the WF model, and based on the Bayes criterion, the equivalent error threshold quantization model of the WF is established for the minimum risk, with consideration of the different losses caused by the misjudgment of the model validity to the power system. Finally, the feasibility of the proposed method is verified by an actual WF example, and compared with the error threshold at home and abroad, the effectiveness of the WF equivalent model can be determined more quickly and accurately.

Keywords

• wind farm equivalent error
• threshold quantization
• bayes criterion
• prior probability
• discriminant loss ratio