Assisting PV Experts in On-Site Condition Evaluation of PV Modules Using Weather-Independent Dark IV String Curves, Artificial Intelligence and a Web-Database

Joachim Rüter (), Felix Meyer, Grit Behrens, Konrad Mertens and Matthias Diehl
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Joachim Rüter: University of Applied Sciences Bielefeld
Felix Meyer: University of Applied Sciences Bielefeld
Grit Behrens: University of Applied Sciences Bielefeld
Konrad Mertens: Münster University of Applied Sciences, Electrical Engineering
Matthias Diehl: Photovoltaikbüro Ternius und Diehl GbR

A chapter in Advances and New Trends in Environmental Informatics, 2022, pp 87-102 from Springer

Abstract: Abstract Photovoltaic (PV) modules can make a huge contribution to achieve the Sustainable Development Goals of the United Nations. To be able to make that contribution, regular check-ups and evaluation of installed PV modules are necessary as they can develop faults and degenerate over time. In this project, we improve the dark IV string curve method used for on-site fault detection and module evaluation. We do so by training artificial intelligence (AI) models to predict the maximum power point and the bright IV curve of PV modules given the weather-independent dark IV string curve. We present some background on this topic, describe the data used for training and the developed models. The results are illustrated graphically. To make the models available for PV experts in practice and to support their decision-making process, we also developed the web-database-application iPVModule for storing historical PV Module data and integrated the AI-models.

Keywords: Artificial intelligence; Deep learning; Photovoltaic; Dark IV string curve; IV curve; Maximum power point; Web; Database; Application; Predictive maintenance (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-030-88063-7_6

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DOI: 10.1007/978-3-030-88063-7_6

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