Intelligent Time-Adaptive Transient Stability Assessment System

TL;DR

This paper presents an LSTM-based transient stability assessment system that adaptively balances accuracy and response time, improving reliability and speed in power system stability evaluation.

Contribution

It introduces a novel temporal self-adaptive scheme for LSTM models, enhancing assessment accuracy while reducing model complexity and training time.

Findings

Improved assessment accuracy over previous methods

Faster training process due to simpler model structure

Effective in three different power system case studies

Abstract

Online identification of post-contingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability assessment system.