Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches

Holger Behrends, Dietmar Millinger, Werner Weihs-Sedivy, Anže Javornik, Gerold Roolfs and Stefan Geißendörfer
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Holger Behrends: German Aerospace Center (DLR), Institute of Networked Energy Systems, 26129 Oldenburg, Germany
Dietmar Millinger: Twingz Development GmbH, 1060 Vienna, Austria
Werner Weihs-Sedivy: Twingz Development GmbH, 1060 Vienna, Austria
Anže Javornik: Pointar d.o.o., 4220 Škofja Loka, Slovenia
Gerold Roolfs: Doepke Schaltgeräte GmbH, 26506 Norden, Germany
Stefan Geißendörfer: German Aerospace Center (DLR), Institute of Networked Energy Systems, 26129 Oldenburg, Germany

Energies, 2022, vol. 15, issue 2, 1-17

Abstract: 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.

Keywords: renewable energies; photovoltaic; predictive maintenance; reliability; anomaly detection; residual current; machine learning; reconstruction error (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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