Deep learning based on Transformer architecture for power system
  short-term voltage stability assessment with class imbalance
Yang Li Jiting Cao Yan Xu Lipeng Zhu Zhao Yang Dong
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.