Design and Analysis of Electricity Anomaly Detection and Diagnosis Models Based on Generative Adversarial Networks

Effective energy management and optimal utilization of assets are crucial for a nation’s progress and prosperity. However, as electricity travels from power plants to end users, it incurs two types of losses: Technical Losses (TL) and Non-Technical Losses (NTL). TLs arise from outdated or inefficient technologies, whereas NTLs result from abnormal electricity usage, including electricity theft, which is often conducted to reduce expenses. These losses pose significant challenges to maintaining grid reliability and result in reduced profits for utility operators. Although the implementation of Automatic Metering Infrastructure (AMI) has improved grid predictability, it has also created new vulnerabilities for NTLs through Cyber-Physical Theft Attacks (CPTA). Machine learning techniques have been applied to identify and mitigate CPTA; however, they often fail to capture comprehensive Energy Consumption Patterns (ECPs), limiting their effectiveness in detecting malicious activities. Generative Adversarial Networks (GANs) have shown remarkable success in various anomaly detection applications. In the context of electricity anomaly identification and diagnosis, GANs are particularly effective in modeling typical energy usage patterns and detecting deviations. Building on this foundation, this study introduces an Adaptive GAN (AGAN) model to detect and diagnose electricity anomalies. The AGAN dynamically optimizes key parameters, such as the number of hidden neurons, epochs and steps per epoch, using the Improved Honey Badger Algorithm (IHBA). This optimization enhances the model’s performance, particularly in terms of Detection Accuracy, Critical Success Index (CSI) and False Omission Rate (FOR). The proposed model demonstrates its capability to accurately identify electricity anomalies and has been rigorously evaluated against existing anomaly detection methods. Results highlight AGAN’s superior effectiveness and robustness, establishing it as a reliable solution for improving the reliability and efficiency of electricity grid systems.