4.5 Article

Edge-based individualized anomaly detection in large-scale distributed solar farms

Journal

ICT EXPRESS
Volume 8, Issue 2, Pages 174-178

Publisher

ELSEVIER
DOI: 10.1016/j.icte.2021.12.011

Keywords

Photovoltaic; Anomalies; Siamese; Edge computing

Funding

  1. American University of Sharjah, UAE [SCRI-18-02]

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This paper presents an anomaly detection system that utilizes Siamese-twin neural networks for detecting anomalies on edge devices in a solar farm. The model achieves high accuracy and efficiency, as evaluated on multiple hardware platforms.
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%. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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