# Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN

arXiv: https://arxiv.org/abs/2112.08836 · HTML: https://mlsanity.com/paper/2112.08836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)

## Built on by (most cited first, 4 of 4)

- [Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting](https://mlsanity.com/paper/2202.06738/) (2022)
- [Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System](https://mlsanity.com/paper/2205.06576/) (2022)
- [Message-passing selection: Towards interpretable GNNs for graph classification](https://mlsanity.com/paper/2306.02081/) (2023)
- [Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network](https://mlsanity.com/paper/2201.09245/) (2022)

## Abstract (arXiv)

Although deep learning has achieved impressive advances in transient stability assessment of power systems, the insufficient and imbalanced samples still trap the training effect of the data-driven methods. This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples. To fit the complex feature distribution of the transient stability samples, the proposed framework firstly models the samples as tabular data and uses Gaussian mixture models to normalize the tabular data. Then we transform multiple conditions into a single conditional vector to enable multi-conditional generation. Furthermore, this paper introduces three evaluation metrics to verify the quality of generated samples based on the proposed framework. Experimental results on the IEEE 39-bus system show that the proposed framework effectively balances the transient stability samples and significantly improves the performance of transient stability assessment models.