A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic
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In: Energies : open-access journal of related scientific research, technology development and studies in policy and management, Vol. 14, No. 5, 1261, 03.2021.
Research output: Contribution to journal › Article › Research › peer-review
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TY - JOUR
T1 - A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic
AU - Gradwohl, Christopher
AU - Dimitrievska, Vesna
AU - Pittino, Federico
AU - Muehleisen, Wolfgang
AU - Montvay, András
AU - Langmayr, Franz
AU - Kienberger, Thomas
N1 - Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - PV system
KW - failure detection
KW - failure diagnostic
KW - operation and maintenance
KW - predictive- and reliability-based maintenance
KW - model-based state detection;
KW - physical model
KW - one-diode model
KW - statistical model
KW - virtual sensors
KW - predictive
KW - model-based state detection
KW - and reliability-based maintenance
KW - One-diode model
KW - Virtual sensors
KW - Operation and maintenance
KW - Predictive and reliability-based maintenance
KW - Physical model
KW - Statistical model
KW - Failure diagnostic
KW - Failure detection
KW - Model-based state detection
UR - http://www.scopus.com/inward/record.url?scp=85107130520&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/en14051261
DO - https://doi.org/10.3390/en14051261
M3 - Article
VL - 14
JO - Energies : open-access journal of related scientific research, technology development and studies in policy and management
JF - Energies : open-access journal of related scientific research, technology development and studies in policy and management
SN - 1996-1073
IS - 5
M1 - 1261
ER -