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<p>The integration of Information and Communication Technologies (ICT) into conventional power grids has given rise to smart grids, which oversee electrical power distribution, generation, and utilization. Despite their benefits, smart grids face communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failures, structural faults, power consumption irregularities, and cyber-attacks. While power systems handle natural disturbances adeptly, identifying anomalies caused by cyber-attacks remains complex. This paper introduces an intelligent Deep Learning (DL) approach for smart grid anomaly detection. Data is initially collected from standard sources such as smart meters, weather stations, and user behavior records. Optimal weighted feature selection is performed using the Modified Flow Direction Algorithm (MFDA) before inputting the selected features into an "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results confirm the model's superior performance, demonstrating a higher detection rate compared to existing methods and enhancing the robustness of the smart grid system.</p>

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Smart Grid Anomaly Detection Using MFDA and Dilated GRU-based Neural Networks

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Mudavath Ravinder,
  • Vikram Kulkarni

摘要

The integration of Information and Communication Technologies (ICT) into conventional power grids has given rise to smart grids, which oversee electrical power distribution, generation, and utilization. Despite their benefits, smart grids face communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failures, structural faults, power consumption irregularities, and cyber-attacks. While power systems handle natural disturbances adeptly, identifying anomalies caused by cyber-attacks remains complex. This paper introduces an intelligent Deep Learning (DL) approach for smart grid anomaly detection. Data is initially collected from standard sources such as smart meters, weather stations, and user behavior records. Optimal weighted feature selection is performed using the Modified Flow Direction Algorithm (MFDA) before inputting the selected features into an "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results confirm the model's superior performance, demonstrating a higher detection rate compared to existing methods and enhancing the robustness of the smart grid system.