Abstract
The incorporation of Information and Communication Technologies (ICT) into traditional power grids has transformed them into smart grids, revolutionizing energy management systems. At the core of this transformation are Intelligent Electronic Devices (IEDs), which provide essential data for key Energy Management System (EMS) applications, such as state estimation and optimal power flow. IEDs are critical for ensuring the stability and security of smart grid operations, but they are also vulnerable to various anomalies, including infrastructure faults, equipment malfunctions, energy theft, and cyberattacks. Detecting these anomalies is vital to maintaining the reliability of smart grid systems and preventing potential threats to national security. This study introduces a statistical data-driven framework designed to detect and explain anomalies in IED-based smart grid systems. The framework includes a preprocessing module for ensuring high-quality input data and an anomaly detection module that prioritizes interpretability and explainability. Using methods like the Gaussian Mixture Model (GMM), Kalman Filter (KF), and ExtraTree Classifier, the framework achieves 99% accuracy in anomaly detection and 88% accuracy in classifying events as either natural occurrences or cyberattacks.
Published Version
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