The Missing Data Recovery Method Based on Improved GAN

Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term Memory (BiLSTM) networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data. The discriminator employs a Convolutional Neural Network (CNN) architecture to integrate local features with global structures, effectively mitigating the generation of implausible imputations. Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmark methods,with an average improvement of 17.7% in reconstruction accuracy. The proposed method significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids, showing great potential for practical applications in power systems.