Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

Abstract

The integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness.