Online transient stability margin prediction of power systems with wind farms using ensemble regression trees. (13th August 2021)

• Record Type: Journal Article
• Authors: Mi, Dengkai; Wang, Tong; Gao, Mingyang; Li, Congcong; Wang, Zengping
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-13
• Subjects: dynamic security assessment -- ensemble learning -- random forest -- transient stability margin; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.13057
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: A new method for online evaluation of the transient stability of wind farms incorporated system based on random forest regression is proposed in this paper. The data before contingency was employed as the inputs instead of the post fault features. The critical clearing time is employed as the transient stability boundary, which determines how stable the system is after the given contingency. The mapping function between the pre‐contingency conditions and the corresponding critical clearing time is modeled as ensemble regression trees model, which consists of lots of base learner. Through the bootstrap method and the random selection of variables in the training process, the problem of dimensionality disaster can be avoided naturally without the need to specifically select features. The out‐of‐bag error generated during the bootstrap process is used for parameter selection and variable importance measures. Case study on the New England 39‐bus system incorporated wind farms and IEEE 118‐bus system shows that the proposed method has a strong prediction accuracy and generalization ability. Abstract : A new method for online evaluation of the transient stability based on random forest regression is proposed in this paper. The mapping function between the pre‐contingency conditions and the corresponding critical clearing time is modeled as ensemble regression trees model, which consists of lots of base learner. Case study shows that the proposed method has a strong prediction accuracy.