A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control

TL;DR

This paper compares three polynomial chaos expansion methods for global sensitivity analysis in power systems, revealing that directly using correlated inputs yields the most accurate results, guiding uncertainty management.

Contribution

It provides a comparative evaluation of PCE-based methods for sensitivity analysis with correlated inputs in power systems, highlighting the most effective approach.

Findings

Correlated input PCE models produce the most accurate ANCOVA indices.

Decorrelated input PCE models may not be the most accurate for sensitivity analysis.

Analysis of errors in different PCE models informs better uncertainty control.

Abstract

In this letter, we compare three polynomial chaos expansion (PCE)-based methods for ANCOVA (ANalysis of COVAriance) indices based global sensitivity analysis for correlated random inputs in two power system applications. Surprisingly, the PCE-based models built with independent inputs after decorrelation may not give the most accurate ANCOVA indices, though this approach seems to be the most correct one and was applied in [1] in the field of civil engineering. In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis. Analysis and discussions about the errors of different PCE-based models will also be presented. These results provide important guidance for uncertainty management and control in power system operation and security assessment.