FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults

Abstract

Transient stability assessment (TSA) is critical to the reliable operation of a power system against severe fault conditions. In practice, TSA based on deep learning is preferable for its high accuracy but often overlooks challenges in maintaining data privacy while coping with network topology changes. This article proposes an innovative f ocal l oss-based multihead a ttention co nvolutional n etwork (FLACON) for accurate post-disturbance TSA under both symmetrical and asymmetrical smart grid faults. The proposed approach effectively incorporates cross-domain deep federated transfer learning (FTL) to leverage local operating data for TSA in a decentralized fashion. By introducing convolutional layers alongside multi-head attention mechanisms, the FLACON framework significantly improves learning efficiency across geographically distributed datasets. To address the challenge of class imbalance, the model integrates a balance factor-enhanced focal loss function. The FTL architecture enables decentralized model training across various clients, thus preserving data privacy and reducing the burden of communication overhead. To avoid the constant adjustment of hyperparameters, the FLACON employs an inductive transfer learning approach for hyperparameter tuning of the pre-trained model, markedly decreasing training time. Extensive experiments on datasets from the IEEE 39-bus system and the IEEE 68-bus system demonstrate FLACON's exceptional accuracy of 98.98% compared to some competitive alternatives.