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

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.