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 -