Online Monitoring System for Photovoltaic Systems Using Anomaly Detection with Machine Learning
Konferenz: NEIS 2019 - Conference on Sustainable Energy Supply and Energy Storage Systems
19.09.2019 - 20.09.2019 in Hamburg, Deutschland
Tagungsband: NEIS 2019
Seiten: 6Sprache: EnglischTyp: PDF
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Autoren:
Benninger, Moritz; Hofmann, Martina; Liebschner, Marcus (Aalen University of Applied Sciences, Beethovenstr. 1, 73430 Aalen, Germany)
Inhalt:
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.