Optimization Research on Anomaly Detection and Localization Algorithm for Complex Power Networks

Efficient power networks are crucial for modern society, ensuring reliable electricity supply to homes, businesses, and industries while supporting economic growth and technological advancements. They also contribute to environmental sustainability by facilitating the transition to renewable energy sources and reducing environmental impact. Moreover, efficient power networks play a critical role in disaster response, healthcare delivery, and communication systems. Given the complexities inherent in power networks, effective anomaly detection is essential to prevent disruptions like power outages and economic losses. To address this, a novel artificial intelligence-based anomaly detection and localization approach is proposed. This approach involves three key steps: preprocessing, feature extraction, and anomaly detection and localization. In the preprocessing step, the signal decomposition technique breaks down the input signal into smaller segments. Statistical features are then extracted from these segments in the feature extraction step. These features are inputted into an Improved Deep Neural Network (IDNN) model for anomaly detection. Finally, an Enhanced Coati Optimization Algorithm (ECOA)-based Support Vector Regression (SVR) model is utilized to locate the exact location of the detected anomaly. The proposed approach is evaluated through various analyses to demonstrate its effectiveness in enhancing situational awareness and resilience, thereby ensuring the reliability and stability of complex power networks.