A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Christopher Gradwohl, Vesna Dimitrievska, Federico Pittino, Wolfgang Muehleisen, András Montvay, Franz Langmayr and Thomas Kienberger
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Christopher Gradwohl: Energy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, Austria
Vesna Dimitrievska: SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria
Federico Pittino: SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria
Wolfgang Muehleisen: SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria
András Montvay: SAL Silicon Austria Labs GmbH, Inffeldgasse 33, 8010 Graz, Austria
Franz Langmayr: Uptime Engineering GmbH, Schoenaugasse 7/2, 8010 Graz, Austria
Thomas Kienberger: Energy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, Austria

Energies, 2021, vol. 14, issue 5, 1-23

Abstract: 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.

Keywords: PV system; failure detection; failure diagnostic; operation and maintenance; predictive- and reliability-based maintenance; model-based state detection; physical model; one-diode model; statistical model; virtual sensors (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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