Online Monitoring System for Photovoltaic Systems Using Anomaly Detection with Machine Learning

Conference: NEIS 2019 - Conference on Sustainable Energy Supply and Energy Storage Systems
09/19/2019 - 09/20/2019 at Hamburg, Deutschland

Proceedings: NEIS 2019

Pages: 6Language: englishTyp: PDF

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Benninger, Moritz; Hofmann, Martina; Liebschner, Marcus (Aalen University of Applied Sciences, Beethovenstr. 1, 73430 Aalen, Germany)

This paper presents a novel method from the field of machine learning for monitoring photovoltaic systems by detecting anomalies. Self-learning algorithms considerably reduce the measuring effort and at the same time offer reliable monitoring of occurring faults. As a prototype, a Raspberry Π is used in combination with a contactless current sensor.