Early Detection and Classification of Hidden Contingencies in Modern Power Systems: A Learning-based Stochastic Hybrid System Approach

TL;DR

This paper presents a learning-based stochastic hybrid system approach for early detection and classification of hidden contingencies in power systems, improving accuracy and speed over existing methods.

Contribution

It introduces a novel LSHS framework that combines machine learning with stochastic hybrid systems for detecting and classifying unseen power system contingencies.

Findings

Enhanced detection accuracy in simulations.

Faster identification of hidden contingencies.

Effective classification of different contingency types.

Abstract

This paper introduces a novel learning-based Stochastic Hybrid System (LSHS) approach for detecting and classifying various contingencies in modern power systems. Specifically, the proposed method is capable of identifying hidden contingencies that cannot be captured by existing sensing and monitoring systems, such as failures in protection systems or line outages in distribution networks. The LSHS approach detects contingencies by analyzing system outputs and behaviors. It then categorizes them based on their impact on the SHS model into physical, control network, and measurement contingencies. The stochastic hybrid system (SHS) model is further extended into an advanced closed-loop framework incorporating both system dynamics and observer-based state estimation error dynamics. Machine learning methods within the LSHS framework are employed for contingency classification and rapid detection. The practicality and effectiveness of the proposed methodology are validated through simulations on an enhanced IEEE-33 bus system. The results demonstrate that the LSHS framework significantly improves the accuracy and speed of contingency detection compared to state-of-the-art methods, offering a promising solution for enhancing power system contingency detection.