A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement

TL;DR

This paper introduces an adaptive sparse polynomial chaos expansion method to efficiently quantify uncertainties in critical clearing time for transient stability, providing accurate probabilistic analysis and stability enhancement insights.

Contribution

The paper presents a novel adaptive sparse polynomial chaos expansion approach for probabilistic transient stability assessment, improving accuracy and computational efficiency over traditional Monte Carlo methods.

Findings

High accuracy and efficiency demonstrated on WSCC 9-bus system

Provides sensitivity analysis for stability enhancement

Validates mitigation measures for transient stability improvement

Abstract

This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis. The proposed method can not only give fast and accurate estimations for the probabilistic characteristics (e.g., mean, variance, probability density function) of the probabilistic CCT (PCCT), but also provides crucial information about the sensitivity of random inputs with respect to the variance of PCCT. Utilizing the sensitivity information, mitigation measures can be developed for transient stability enhancement. Numerical studies on the WSCC 9-bus system demonstrate the high accuracy and efficiency of the proposed method compared to the Monte Carlo simulation method. The provided sensitivity information and the effectiveness of mitigation measures in transient stability enhancement are also verified.