Adaptive Online Learning with Momentum for Contingency-based Voltage Stability Assessment

TL;DR

This paper introduces an adaptive online deep learning framework with momentum for real-time voltage stability assessment in power systems, effectively handling topology changes and improving accuracy over traditional methods.

Contribution

It develops a measurement-based, adaptive deep learning approach with momentum for voltage stability assessment that accounts for topology changes and enhances prediction accuracy.

Findings

The proposed method achieves high accuracy in voltage stability assessment.

Adaptive algorithms outperform traditional nonadaptive methods.

Numerical tests on a 68-bus system validate effectiveness.

Abstract

Voltage stability refers to the ability of a power system to maintain acceptable voltages among all buses under normal operating conditions and after a disturbance. In this paper, a measurement-based voltage stability assessment (VSA) framework using online deep learning is developed. Since the topology changes induced by transmission contingencies may significantly reduce the voltage stability margin, different network topologies under different operating conditions are involved in our training dataset. To achieve high accuracy in the training process, a gradient-based adaptive learning algorithms is adopted. Numerical results based on the NETS-NYPS 68-bus system demonstrate the effectiveness of the proposed VSA approach. Moreover, with the proximal function modified adaptively, the adaptive algorithm with momentum outperforms traditional nonadaptive algorithms whose learning rate is constant.