Mix Copula Function Based Wind Power Correlation Analysis: A Bayesian Linear Regression Approach

doi:10.11930/j.issn.1004-9649.201907121

Abstract

Abstract: Analyzing the output correlation among wind farms is beneficial to reasonably plan the power transmission and scheduling optimization, so as to improve the utilization rate of transmission lines. Taking wind field in northern Hebei region as an example, this paper analyzes the characteristics of wind power first, and then improves a mix Copula method to model the relationship among the wind power correlation structure. What’s more, we use the Bayesian linear regression method to establish a mixed Copula function model to calculate the correlation of wind speed sequences from different wind farm groups. In this way, we can fit the joint distribution function between them and analyze the impact of correlation on the joint output of wind farms. In addition, this method has been verified to be effective and accurate, it was also be compared with other correlation function modeling. The results show that the mixed Copula function model based on Bayesian linear regression can well calculate the correlation of wind power output, from which the output probability distribution obtained can get more accurate fitting results.

Key words: Bayesian linear regression, correlation, mixed Copula function, wind power, output probability

SU Chenbo, LIU Chongru, XU Shitian, YUE Hao. Mix Copula Function Based Wind Power Correlation Analysis: A Bayesian Linear Regression Approach[J]. Electric Power, 2021, 54(8): 182-189.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201907121

https://www.electricpower.com.cn/EN/Y2021/V54/I8/182

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