Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems
This study presents a systematic approach for examining the performance and vulnerability of large-scale, grid-connected PV systems in relation to inverter faults − particularly those linked to insulated-gate bipolar transistor (IGBT) component. The focus is on an interdisciplinary approach, utilisi...
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| Format: | Article |
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EDP Sciences
2025-01-01
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| Series: | EPJ Photovoltaics |
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| Online Access: | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240070/pv20240070.html |
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| author | Malik Stephanie Daßler David Patel Dharm Klute Carola Klengel Robert Dietrich Andreas Kaufmann Kai Hennig Carsten Wehnert Danny Ebert Matthias |
| author_facet | Malik Stephanie Daßler David Patel Dharm Klute Carola Klengel Robert Dietrich Andreas Kaufmann Kai Hennig Carsten Wehnert Danny Ebert Matthias |
| author_sort | Malik Stephanie |
| collection | DOAJ |
| description | This study presents a systematic approach for examining the performance and vulnerability of large-scale, grid-connected PV systems in relation to inverter faults − particularly those linked to insulated-gate bipolar transistor (IGBT) component. The focus is on an interdisciplinary approach, utilising methodologies from materials science, data analysis, statistics, and machine learning to investigate defect mechanisms, identify recurring issues, and analyse their impacts for a system portfolio of 64 MWp. A root cause analysis identified the failure pattern through material diagnostics of several power modules from inverters previously installed in the field. Prolonged exposure to high temperatures led to the degradation of the IGBT semiconductors, resulting in a breakthrough due to the short-term release of excessive heat. In parallel, an impact analysis was carried out based on historical monitoring data, that identified a faulty control behaviour of the inverter during curtailment. Due to the sharp increase in curtailment occurrences, a correlation of this observation was noted across nearly the entire portfolio. Finally, the study explored whether this randomly observed fault pattern that led to the inverter failure could have been detected in the data without prior knowledge of it. To achieve this, a method combining an artificial neural network and density-based clustering was proposed to automatically detect this recurring and propagating error pattern. This process was carried out in three steps: predicting the normal behaviour of the inverter, distinguishing between normal behaviour and anomalous behaviour, and differentiating the anomalous behaviour. The fault patterns were clearly assigned to four clusters. By introducing a scalable, data-driven fault diagnostics method, this study highlights how advanced materials science and data analytics can improve early fault detection and maintenance in PV portfolio monitoring, while also providing a deeper understanding of defect mechanisms. These combined approaches ultimately enhance inverter reliability and operational efficiency. |
| format | Article |
| id | doaj-art-72f1385add7e45a38c73e0e72fe2ff17 |
| institution | OA Journals |
| issn | 2105-0716 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Photovoltaics |
| spelling | doaj-art-72f1385add7e45a38c73e0e72fe2ff172025-08-20T02:05:52ZengEDP SciencesEPJ Photovoltaics2105-07162025-01-01162510.1051/epjpv/2025011pv20240070Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systemsMalik Stephanie0https://orcid.org/0009-0005-5150-7017Daßler David1https://orcid.org/0000-0002-2823-5237Patel Dharm2https://orcid.org/0009-0002-6195-7562Klute Carola3https://orcid.org/0009-0007-8190-4245Klengel Robert4https://orcid.org/0009-0009-3791-2891Dietrich Andreas5https://orcid.org/0009-0008-4103-1037Kaufmann Kai6https://orcid.org/0000-0002-1262-7578Hennig Carsten7https://orcid.org/0009-0005-1239-7708Wehnert Danny8https://orcid.org/0009-0006-9079-962XEbert Matthias9https://orcid.org/0000-0002-0910-0652Fraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsFraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsFraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsFraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsFraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsDiSUN, Deutsche Solarservice GmbHDENKweit GmbHSaferay holding GmbHLeipziger Energiegesellschaft mbH & Co. KGFraunhofer IMWS, Fraunhofer Institute for Microstructure of Materials and SystemsThis study presents a systematic approach for examining the performance and vulnerability of large-scale, grid-connected PV systems in relation to inverter faults − particularly those linked to insulated-gate bipolar transistor (IGBT) component. The focus is on an interdisciplinary approach, utilising methodologies from materials science, data analysis, statistics, and machine learning to investigate defect mechanisms, identify recurring issues, and analyse their impacts for a system portfolio of 64 MWp. A root cause analysis identified the failure pattern through material diagnostics of several power modules from inverters previously installed in the field. Prolonged exposure to high temperatures led to the degradation of the IGBT semiconductors, resulting in a breakthrough due to the short-term release of excessive heat. In parallel, an impact analysis was carried out based on historical monitoring data, that identified a faulty control behaviour of the inverter during curtailment. Due to the sharp increase in curtailment occurrences, a correlation of this observation was noted across nearly the entire portfolio. Finally, the study explored whether this randomly observed fault pattern that led to the inverter failure could have been detected in the data without prior knowledge of it. To achieve this, a method combining an artificial neural network and density-based clustering was proposed to automatically detect this recurring and propagating error pattern. This process was carried out in three steps: predicting the normal behaviour of the inverter, distinguishing between normal behaviour and anomalous behaviour, and differentiating the anomalous behaviour. The fault patterns were clearly assigned to four clusters. By introducing a scalable, data-driven fault diagnostics method, this study highlights how advanced materials science and data analytics can improve early fault detection and maintenance in PV portfolio monitoring, while also providing a deeper understanding of defect mechanisms. These combined approaches ultimately enhance inverter reliability and operational efficiency.https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240070/pv20240070.htmlinverter failurematerial diagnosticsdata analysisfault detectioncurtailmentmachine learning |
| spellingShingle | Malik Stephanie Daßler David Patel Dharm Klute Carola Klengel Robert Dietrich Andreas Kaufmann Kai Hennig Carsten Wehnert Danny Ebert Matthias Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems EPJ Photovoltaics inverter failure material diagnostics data analysis fault detection curtailment machine learning |
| title | Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems |
| title_full | Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems |
| title_fullStr | Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems |
| title_full_unstemmed | Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems |
| title_short | Analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large-scale solar energy systems |
| title_sort | analysis of fault detection and defect categorization in photovoltaic inverters for enhanced reliability and efficiency in large scale solar energy systems |
| topic | inverter failure material diagnostics data analysis fault detection curtailment machine learning |
| url | https://www.epj-pv.org/articles/epjpv/full_html/2025/01/pv20240070/pv20240070.html |
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