# Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance

arXiv: https://arxiv.org/abs/2310.11690 · HTML: https://mlsanity.com/paper/2310.11690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [An empirical study on evaluation metrics of generative adversarial networks](https://mlsanity.com/paper/1806.07755/) (2018)
- [Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach](https://mlsanity.com/paper/2107.07738/) (2021)
- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2103.03729/) (2021)
- [Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response](https://mlsanity.com/paper/2301.08861/) (2023)
- [Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game](https://mlsanity.com/paper/2112.07103/) (2021)
- [PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning](https://mlsanity.com/paper/2308.03953/) (2023)
- [Voltage Instability Prediction Using a Deep Recurrent Neural Network](https://mlsanity.com/paper/1908.05554/) (2019)

## Built on by (most cited first, 5 of 5)

- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network](https://mlsanity.com/paper/2408.10181/) (2024)
- [OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model](https://mlsanity.com/paper/2506.18006/) (2025)
- [usfAD Based Effective Unknown Attack Detection Focused IDS Framework](https://mlsanity.com/paper/2403.11180/) (2024)
- [QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks](https://mlsanity.com/paper/2512.09936/) (2025)

## Abstract (arXiv)

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed {as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes}. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. {Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy}. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges.