Online monitoring of the voltage stability margin using local minimax concave penalty regression and adaptive database. (July 2023)

• Record Type: Journal Article
• Authors: Huang, Zongwu; Xu, Xun; Fan, Youping; Wang, Zijiang; Shang, Ben; Shang, Maolin; Yu, Wenxiang; Wu, Yiming; Li, Dongjie; Lin, Mingqi
• Is Part Of: International journal of electrical power & energy systems. Volume 149(2023)
• Publication Date: 2023-07
• Subjects: Voltage stability margin -- Local regression -- Minimax concave penalty -- Adaptive database -- Online monitoring; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2023.109046
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel scheme is proposed for online monitoring of the voltage stability margin. An adaptive database is designed to enhance the generalization ability of the scheme. The proposed scheme can provide reliable voltage stability margin prediction values online. Abstract: A novel online monitoring method for the voltage stability margin (VSM) based on local minimax concave penalties (LocMCP) regression and adaptive database is proposed to ameliorate the weak model interpretability and insufficient generalization ability of the existing online monitoring methods. In this paper, the minimax concave penalty (MCP) is applied for VSM online monitoring for the first time. Compared with the multiple linear regression model (MLRM) and the local least absolute shrinkage and selection operator (LocLASSO), better prediction accuracy can be obtained through LocMCP. The MCP and local regression are combined to find the mapping relationship between VSM and reactive power reserves (RPRs) and to obtain the VSM online monitoring model composed of various local models. To further enhance the generalization ability of the model, an adaptive database is proposed. The data updating of the database is triggered when the local root mean square error (LocRMSE) exceeds the limit or is triggered in the case that the planned operation is performed. Furthermore, the local model can be updated according to the data updating results of the database. The 3-bus system, IEEE 30-bus system, and 1951-bus system are selected for verification, and the test results show that the proposed method is effective and has better generalization ability compared with other parameter regression methods.