Delay Aware Intelligent Transient Stability Assessment System

TL;DR

This paper introduces a delay-aware machine learning system using ensemble LSTM networks for faster, accurate transient stability assessment in power systems, effectively handling communication delays and measurement noise.

Contribution

It presents a novel ensemble LSTM-based system that accounts for communication delays, enabling quicker and robust transient stability assessments compared to existing methods.

Findings

Assessment time reduced by one third

High accuracy maintained despite delays

Robust against measurement noise

Abstract

Transient stability assessment is a critical tool for power system design and operation. With the emerging advanced synchrophasor measurement techniques, machine learning methods are playing an increasingly important role in power system stability assessment. However, most existing research makes a strong assumption that the measurement data transmission delay is negligible. In this paper, we focus on investigating the influence of communication delay on synchrophasor-based transient stability assessment. In particular, we develop a delay aware intelligent system to address this issue. By utilizing an ensemble of multiple long short-term memory networks, the proposed system can make early assessments to achieve a much shorter response time by utilizing incomplete system variable measurements. Compared with existing work, our system is able to make accurate assessments with a significantly improved efficiency. We perform numerous case studies to demonstrate the superiority of the proposed intelligent system, in which accurate assessments can be developed with time one third less than state-of-the-art methodologies. Moreover, the simulations indicate that noise in the measurements has trivial impact on the assessment performance, demonstrating the robustness of the proposed system.