A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic
Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/5/1261 |
| _version_ | 1827588272927277056 |
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| author | Christopher Gradwohl Vesna Dimitrievska Federico Pittino Wolfgang Muehleisen András Montvay Franz Langmayr Thomas Kienberger |
| author_facet | Christopher Gradwohl Vesna Dimitrievska Federico Pittino Wolfgang Muehleisen András Montvay Franz Langmayr Thomas Kienberger |
| author_sort | Christopher Gradwohl |
| collection | DOAJ |
| description | Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants. |
| first_indexed | 2024-03-09T00:32:08Z |
| format | Article |
| id | doaj.art-0830ca951dfe4b699e397f47f0c0c97f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:32:08Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0830ca951dfe4b699e397f47f0c0c97f2023-12-11T18:23:23ZengMDPI AGEnergies1996-10732021-02-01145126110.3390/en14051261A Combined Approach for Model-Based PV Power Plant Failure Detection and DiagnosticChristopher Gradwohl0Vesna Dimitrievska1Federico Pittino2Wolfgang Muehleisen3András Montvay4Franz Langmayr5Thomas Kienberger6Energy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, AustriaSAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, AustriaSAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, AustriaSAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, AustriaSAL Silicon Austria Labs GmbH, Inffeldgasse 33, 8010 Graz, AustriaUptime Engineering GmbH, Schoenaugasse 7/2, 8010 Graz, AustriaEnergy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, AustriaPhotovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.https://www.mdpi.com/1996-1073/14/5/1261PV systemfailure detectionfailure diagnosticoperation and maintenancepredictive- and reliability-based maintenancemodel-based state detection |
| spellingShingle | Christopher Gradwohl Vesna Dimitrievska Federico Pittino Wolfgang Muehleisen András Montvay Franz Langmayr Thomas Kienberger A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic Energies PV system failure detection failure diagnostic operation and maintenance predictive- and reliability-based maintenance model-based state detection |
| title | A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic |
| title_full | A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic |
| title_fullStr | A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic |
| title_full_unstemmed | A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic |
| title_short | A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic |
| title_sort | combined approach for model based pv power plant failure detection and diagnostic |
| topic | PV system failure detection failure diagnostic operation and maintenance predictive- and reliability-based maintenance model-based state detection |
| url | https://www.mdpi.com/1996-1073/14/5/1261 |
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