Microgrids provide a resilient and efficient alternative to traditional power grids, yet they remain vulnerable to operational anomalies, electrical faults, and cybersecurity threats. This study proposes a machine learning-based anomaly detection system utilizing an autoencoder neural network to monitor and analyze microgrid sensor data, specifically current, voltage, and communication traffic. The system learns normal operating patterns and identifies deviations indicative of potential failures or cyber threats. The system undergoes preprocessing techniques such as outlier removal and normalization to ensure data quality, followed by autoencoder-based feature extraction and threshold-based anomaly classification. The proposed framework is implemented as a web-based platform, featuring a Flask-based backend for real-time anomaly detection, a React.js dashboard for intuitive visualization, and a REST API for seamless data exchange. The aim of the research is to enhance the microgrid resilience, reduce downtime, and improve predictive maintenance through the integration of machine learning, real-time data processing, and user-friendly visualization. Experimental results are proposed and serve to assess the accuracy in anomaly detection. The system identifies the deviations and classifies them into low, medium, and high-severity alerts.
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