中国电力2025，Vol.58Issue(4)：98-106,9.DOI:10.11930/j.issn.1004-9649.202406035
基于Bayes判别准则的风电场等值误差阈值最小风险量化方法
Minimum Risk Quantification Method for Equivalent Error Threshold of Wind Farm Based on Bayes Criterion
摘要
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.关键词
风电场等值误差/阈值量化/Bayes判别准则/先验概率/判别损失比Key words
wind farm equivalent error/threshold quantization/Bayes criterion/prior probability/discriminant loss ratio引用本文复制引用
朱乾龙,金小强,王绪利,苏凡亚,邓天白,陶骏..基于Bayes判别准则的风电场等值误差阈值最小风险量化方法[J].中国电力,2025,58(4):98-106,9.基金项目
安徽省高等学校科学研究项目(2023AH050075,2022AH050105). This work is supported by Anhui Province Higher Education Science Research Project(No.2023AH050075,No.2022AH050105). （2023AH050075,2022AH050105）
