Classifying Failures in Distributed Photovoltaic Installations Using a Delphi-Based Dimension-Adjusted Fuzzy SIWEC–MABAC Framework

Distributed photovoltaic (PV) systems are increasingly important for renewable-energy transformation, prosumer participation, and low-emission electricity markets. However, failures affecting inverters, DC-side components, connectors, protection devices, monitoring units, and PV modules may reduce generation continuity, increase service burden, and weaken user confidence in distributed generation. Previous PV-failure studies have mainly identified failure modes or ranked them according to maintenance priority, whereas service companies require actionable classes linked with inspection intervals and corrective actions. This study develops and empirically applies a Delphi-based dimension-adjusted fuzzy SIWEC-MABAC decision-support framework for classifying PV installation failures into maintenance action classes. The procedure combines a completed three-round Delphi expert panel, linguistic uncertainty modelling using dimension-adjusted fuzzy sets, SIWEC criterion weighting, and MABAC ranking based on distance from the border approximation area. The empirical SIWEC–MABAC results show that safety/fire impact, downtime duration, and detectability difficulty dominate the service classification. Arc-fault-related DC-side damage and cable insulation degradation are assigned to immediate corrective action, whereas junction-box overheating, inverter hardware failure, melted MC4 connectors, hot-spot formation, and DC circuit-breaker failure require short-term preventive inspection. The validated framework supports service triage and inspection scheduling rather than real-time fault detection. A one-dimensional four-cluster check reproduced the same class membership, and TOPSIS cross-validation showed strong rank agreement with MABAC (Spearman rho = 0.951).