Edge-based individualized anomaly detection in large-scale distributed solar farms
Ali Reza Sajun, Salsabeel Shapsough, Imran Zualkernan, Rached Dhaouadi
Abstract
Power output from large-scale solar farms is often plagued by anomalies that can adversely impact grid integration. This paper presents an anomaly detection system that used Siamese-twin neural networks for anomaly detection on edge devices in a solar farm. The model achieved an F1-score of 0.88 and was evaluated using two multi-threading schemes on a Raspberry PI, Nvidia Nano and Google Coral. A single analytics edge device could service 512 solar panels at 1 Hz. The best hardware platform was Nvidia’s Nano using a TensorFlow Lite model consuming about 35 Wh over 12 h, and with maximum CPU utilization not exceeding 60%.
Topics & Concepts
Anomaly detectionEnhanced Data Rates for GSM EvolutionAnomaly (physics)Scale (ratio)Computer scienceAnalyticsGridEmbedded systemArtificial intelligenceData miningPhysicsCartographyGeologyGeographyGeodesyCondensed matter physicsPhotovoltaic System Optimization TechniquesSmart Grid Energy ManagementSolar Radiation and Photovoltaics