Maintenance Prioritization in Photovoltaic Installations Using a Hybrid Particle Swarm Optimization–Failure Mode and Effects Analysis–VIKOR Decision Framework.
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| Title: | Maintenance Prioritization in Photovoltaic Installations Using a Hybrid Particle Swarm Optimization–Failure Mode and Effects Analysis–VIKOR Decision Framework. |
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| Authors: | Kut, Paweł1 (AUTHOR), Pietrucha-Urbanik, Katarzyna1 (AUTHOR) kpiet@prz.edu.pl, Rabczak, Sławomir1 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2622. 25p. |
| Subject Terms: | *Particle swarm optimization, *Failure mode & effects analysis, *Risk assessment, *Multiple criteria decision making, *Photovoltaic power systems |
| Abstract: | Photovoltaic (PV) installations require maintenance prioritization models capable of ranking technically diverse failure modes under operational, safety, and serviceability constraints. Conventional Failure Mode and Effects Analysis (FMEA) approaches often cannot integrate downtime, cost, safety, and detectability into a single transparent workflow. This study develops a hybrid Particle Swarm Optimization (PSO)-FMEA-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) framework for maintenance prioritization of eight representative PV failure modes. Classical FMEA was used as a diagnostic baseline, a seven-criterion maintenance matrix was constructed, PSO calibrated criteria weights, and a criticality-oriented VIKOR compromise-ranking procedure generated the final ordering. Semi-empirical operational and service log evidence was used only to anchor the interpretation of occurrence, direct cost, and downtime; it was not treated as a real-time fault-detection dataset. The results identified inverter overvoltage shutdown/grid incompatibility, cable insulation degradation, and junction box overheating as the highest maintenance priorities. Their ordering differed from classical Risk Priority Number (RPN) results, showing that frequency alone does not adequately represent maintenance urgency. Sensitivity analysis confirmed the stability of the two leading alternatives under different VIKOR strategy parameters. The framework provides a discriminative decision support tool for inspection planning, service scheduling, and corrective-action targeting in grid-connected PV systems, while further validation on larger Supervisory Control and Data Acquisition (SCADA)- or inverter-log-based datasets remains necessary. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Photovoltaic (PV) installations require maintenance prioritization models capable of ranking technically diverse failure modes under operational, safety, and serviceability constraints. Conventional Failure Mode and Effects Analysis (FMEA) approaches often cannot integrate downtime, cost, safety, and detectability into a single transparent workflow. This study develops a hybrid Particle Swarm Optimization (PSO)-FMEA-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) framework for maintenance prioritization of eight representative PV failure modes. Classical FMEA was used as a diagnostic baseline, a seven-criterion maintenance matrix was constructed, PSO calibrated criteria weights, and a criticality-oriented VIKOR compromise-ranking procedure generated the final ordering. Semi-empirical operational and service log evidence was used only to anchor the interpretation of occurrence, direct cost, and downtime; it was not treated as a real-time fault-detection dataset. The results identified inverter overvoltage shutdown/grid incompatibility, cable insulation degradation, and junction box overheating as the highest maintenance priorities. Their ordering differed from classical Risk Priority Number (RPN) results, showing that frequency alone does not adequately represent maintenance urgency. Sensitivity analysis confirmed the stability of the two leading alternatives under different VIKOR strategy parameters. The framework provides a discriminative decision support tool for inspection planning, service scheduling, and corrective-action targeting in grid-connected PV systems, while further validation on larger Supervisory Control and Data Acquisition (SCADA)- or inverter-log-based datasets remains necessary. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19112622 |
