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Analysis Of Residual Current Flows In Inverter Based Energy Systems Using Machine Learning Approaches

Behrends, Holger and Millinger, Dietmar and Weihs-Sedivy, Werner and Javornik, Anze and Roolfs, Gerold and Geissendörfer, Stefan (2022) Analysis Of Residual Current Flows In Inverter Based Energy Systems Using Machine Learning Approaches. Energies, 15 (2). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en15020582. ISSN 1996-1073.

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Official URL: https://www.mdpi.com/1996-1073/15/2/582


Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

Item URL in elib:https://elib.dlr.de/148316/
Document Type:Article
Title:Analysis Of Residual Current Flows In Inverter Based Energy Systems Using Machine Learning Approaches
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Behrends, HolgerGerman Aerospace Center, Institute of Networked Energy Systemshttps://orcid.org/0000-0002-2358-5596UNSPECIFIED
Geissendörfer, StefanGerman Aerospace Center, Institute of Networked Energy Systemshttps://orcid.org/0000-0002-7496-8191UNSPECIFIED
Date:14 January 2022
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Artificial Intelligence and Smart Energy
Keywords:renewable energies; photovoltaic; predictive maintenance; reliability; anomaly detection; residual current; machine learning; reconstruction error
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Behrends, Holger
Deposited On:18 Jan 2022 16:50
Last Modified:18 Jan 2022 16:50

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