Deep learning anomaly detection in AI-powered intelligent power distribution systems

Introduction: Intelligent power distribution systems are vital in the modern power industry, tasked with managing power distribution efficiently. These systems, however, encounter challenges in anomaly detection, hampered by the complexity of data and limitations in model generalization. Methods: This study developed a Transformer-GAN model that combines Transformer architectures with GAN technology, efficiently processing complex data and enhancing anomaly detection. This model’s self-attention and generative capabilities allow for superior adaptability and robustness against dynamic data patterns and unknown anomalies. Results: The Transformer-GAN model demonstrated remarkable efficacy across multiple datasets, significantly outperforming traditional anomaly detection methods. Key highlights include achieving up to 95.18% accuracy and notably high recall and F1 scores across diverse power distribution scenarios. Its exceptional performance is further underscored by achieving the highest AUC of 96.64%, evidencing its superior ability to discern between normal and anomalous patterns, thereby reinforcing the model’s advantage in enhancing the security and stability of smart power systems. Discussion: The success of the Transformer-GAN model not only boosts the stability and security of smart power distribution systems but also finds potential applications in industrial automation and the Internet of Things. This research signifies a pivotal step in integrating artificial intelligence into the power sector, promising to advance the reliability and intelligent evolution of future power systems.