Advanced Diagnosis of Photovoltaic Plants: A Systematic Review of Machine Learning in Thermography, Electroluminescence, and I-V Curves Utilizing UAV and IoT Platforms

The rapid expansion of utility-scale and commercial photovoltaic (PV) plants demands high-efficiency predictive and proactive maintenance schemes to safeguard operational availability and minimize degradation losses. Photovoltaic generators are subject to progressive mechanical, thermal, and electrical stressors that lead to cell microcracks, solder fatigue, active bypass diode failures, and localized hotspots. This article presents a rigorous systematic literature review conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. The review critically synthesizes the integration of machine learning and deep learning models with three primary physical diagnostic modalities: current–voltage (I-V) curve tracing, infrared thermography (terrestrial and aerial, aIRT), and optical luminescence (electroluminescence, EL, and photoluminescence, PL), alongside cross-cutting low-cost Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) acquisition platforms. From an initial identification of 299 records in the Scopus database, a structured multi-stage screening and quality assessment pipeline resulted in the definitive inclusion of 39 primary empirical research studies published between 2022 and 2025 (with active tracking extending through early 2026). This review establishes a formal four-pillar quality assessment framework (evaluating experimental rigor, methodological transparency, component disaggregation, and reproducibility) and carries out a multidimensional synthesis across diagnostic accuracy, computational complexity, inference latency, inspection throughput, and operational invasiveness. The findings reveal that while single-modality aerial thermography provides unmatched spatial screening speeds (5–10 h for a 4 MW plant), it remains blind to non-dissipative microscopic fractures and exhibits ambiguity between temporary shading and internal cell degradation. Conversely, modular online I-V tracing and aerial EL deliver definitive electrical quantification and sub-cellular resolution, respectively, but face severe throughput and operational invasiveness constraints. Furthermore, this review formalizes a structured four-paradigm taxonomy of multimodal fusion (early, intermediate, late, and hierarchical) and evaluates practical engineering deployment bottlenecks—including legacy inverter topology constraints, communication bandwidth limits, algorithmic domain shift across emerging cell architectures (PERC, TOPCon, HJT, and bifacials), and outdoor thermal metrological uncertainty. Finally, an actionable technological roadmap and conceptual architectural blueprint toward automated multimodal digital twins are outlined alongside an explicit analysis of review limitations.