Multi-indicator inference scheme for fuzzy assessment of power system transient stability

Tingjian Liu; Youbo Liu; Junyong Liu; Yue Yang; Gareth A. Taylor; Zhengwen Huang

doi:10.17775/CSEEJPES.2016.00029

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Multi-indicator inference scheme for fuzzy assessment of power system transient stability

更新时间：2025-12-18

- Multi-indicator inference scheme for fuzzy assessment of power system transient stability
- Multi-indicator inference scheme for fuzzy assessment of power system transient stability
- 中国电机工程学会电力与能源系统学报(英文) 2016年2卷第3期页码：1-9
- 作者机构：
  
  1. Sichuan University,,, CN,Sichuan,Chengdu
  2. Brunel University, Uxbridge, Middlesex,GB
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00029
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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Tingjian Liu, Youbo Liu, Junyong Liu, 等. Multi-indicator inference scheme for fuzzy assessment of power system transient stability[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):1-9.

Tingjian Liu, Youbo Liu, Junyong Liu, et al. Multi-indicator inference scheme for fuzzy assessment of power system transient stability[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 1-9.
Tingjian Liu, Youbo Liu, Junyong Liu, 等. Multi-indicator inference scheme for fuzzy assessment of power system transient stability[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):1-9. DOI： 10.17775/CSEEJPES.2016.00029.

Tingjian Liu, Youbo Liu, Junyong Liu, et al. Multi-indicator inference scheme for fuzzy assessment of power system transient stability[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 1-9. DOI： 10.17775/CSEEJPES.2016.00029.

摘要

Abstract

A multi-indicator inference scheme is proposed in this paper to achieve an intuitive assessment of post-fault transient stability of power systems. The proposed scheme uses the fuzzy inference technique to classify the stability level as “safe

” “low-risk

” “high-risk

” and “danger.” A multi-criteria quality assessment method is first introduced. Several transient indicators are then proposed as assessment criteria. To select the effective indicators for assessment

correlation mining using univariate regression analysis is performed between each indicator and a critical clearance time (CCT)-based stability index. The fuzzy sets of indicators for different stability levels are then determined according to their correlations with the stability index. The weighting factors of indicators are also allocated according to their regression error in correlation mining. The proposed inference scheme is further demonstrated and its effectiveness is validated in case studies on IEEE 68-bus system and a 756-bus transmission system in China.

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