Probabilistic Assessment of Rare Transient Instability Events via Kriging-based Active Learning Framework

TL;DR

This paper introduces a Kriging-based active learning framework to efficiently identify and estimate the probability of rare transient instability events in power systems under uncertainty.

Contribution

It presents a novel active learning approach that accurately characterizes rare instability regions with fewer simulations, improving efficiency over existing methods.

Findings

The framework outperforms random forest-based active learning in accuracy.

It requires fewer simulations to estimate small instability probabilities.

Validated on IEEE 59-bus and WECC 240-bus systems with real-world data.

Abstract

The increasing uncertainty in modern power systems, driven by the integration of intermittent energy sources and variable loads, underscores the need for probabilistic transient stability assessment. However, existing assessment methods primarily focus on average system stability behavior and may struggle or incur high computational cost when identifying rare transient instability events, which in turn are critical for ensuring system resilience. To address this, the paper proposes a Kriging-based active learning framework to accurately characterize rare instability regions within the input uncertainty space and estimate the associated small instability probability, while requiring only a limited number of expensive time-domain simulations. The proposed active learning (AL) framework is tested on a modified IEEE 59-bus system with simulated load and wind uncertainties, and a WECC 240-bus system incorporating real-world wind and solar generation data. Comparative studies with the existing random forest-based active learning method and three non-AL methods demonstrate that the proposed AL framework achieves superior accuracy and computational efficiency.