Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment

TL;DR

This paper introduces a novel data augmentation method using Variational Mode Decomposition to enhance deep neural network training for power system stability assessment, addressing data imbalance issues.

Contribution

It proposes a new augmentation technique that preserves dynamic characteristics, validated through distribution similarity tests and improved neural network performance.

Findings

Augmented data closely matches original data distribution.

Enhanced DNN performance with augmented data.

Effective mitigation of data imbalance in power system analysis.

Abstract

Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder's performance employing several metrics.