Photovoltaic modules in desert areas benefit from high irradiation levels but suffer from harsh environmental stress factors, which influence the Levelized Cost of Electricity by decreasing the lifetime and performance and increasing the maintenance costs. Using optimized half-cell module designs mounted in the most efficient orientation according to the plant requirements can lead to reduced production costs, increased energy yield and longer service lives for PV modules in desert areas. In this work, we review the technical advantages of half-cell modules in desert regions and discuss the potential gains in levelized costs of electricity due to reduced material consumption, a higher cell-to-module power ratio, lower module temperatures, better yields, reduced cleaning cycles and finally, reduced fatigue in interconnection due to thermal cycling. We show that half-cell modules are the most cost-effective option for desert areas and are expected to have a relevant lower Levelized Cost of Electricity.

Photovoltaic (PV) system reliability and durability investigations are essential for industrial maturity and economic success. Recently, PV systems received much interest in Iraq due to many reasons—for instance, power shortage, global warming and pollution. Despite this interest, the precise economic implications of PV system reliability in harsh climates like Iraq remain uncertain. This work assesses the economic implications of PV system component reliability and soiling in Iraq using field experience and historical data. This study identifies the most common failure modes of PV systems installed in Iraq and similar climatic regions, and also ranks their severity. Simulations explore scenarios of PV module degradation rates, inverter lifetimes, soiling rates, and cleaning intervals, revealing that soiling has the most detrimental effect, with cleaning frequency leading to Levelized Cost of Electricity (LCOE) losses of over 30%, depending on the location. Inverter lifetime contributes to LCOE losses between 4 and 6%, depending on the PV system’s location. This study also evaluates the impact of tilt angle as a mitigation strategy for reducing soiling loss and its economic implications, finding that installing PV modules at higher tilt angles could reduce the economic impact of soiling by approximately 4.4%. Additionally, the optimal cleaning strategy identified is fully automated dry cleaning with robots, outperforming other strategies economically. Overall, the findings highlight that the LCOE in Iraq is relatively high compared to the global weighted average for utility-scale PV systems, primarily due to high soiling and degradation rates. The LCOE varies within the country, influenced by different degradation rates. This study aims to assist PV stakeholders in Iraq and the Middle East and North Africa (MENA) region in accurately estimating solar bankability; moreover, increasing reliability by minimizing the technical and financial risks by considering key parameters specific to these regions.

Modules with partial cells show better performance compared to full-cell modules due to lower electrical losses and increased optical gains. In this paper, we compare photovoltaic (PV) modules made of full, half, and third cells with tab widths of 0.8 and 1.5 mm under moderate and desert conditions, respectively. Initially, a statistical assessment of indoor characterization results of the modules is presented. The efficiency and fill factor of the modules are evaluated to analyze the electrical and optical losses of the modules. It is shown that the modules with partial cells and narrower tab widths show higher efficiencies and implying cell-to-module power ratios over 100%. The modules are characterized under different intensity levels to assess their performance under various illumination conditions. Based on the measured data, the energy yields in Morocco and Germany representing desert and moderate climates, respectively, are calculated. It is demonstrated that increased energy yield can be achieved by selecting appropriate module design and optimized tab width for each climate. The optimized half-cell modules show a yield gain of 3.77% and 4.51% compared to full-cell modules in moderate and desert climates, respectively. These values rise to 4.72% and 5.68% by moving to third-cell modules, respectively.

This study presents a systematic approach for examining the performance and vulnerability of large-scale, grid-connected PV systems in relation to inverter faults − particularly those linked to insulated-gate bipolar transistor (IGBT) component. The focus is on an interdisciplinary approach, utilising methodologies from materials science, data analysis, statistics, and machine learning to investigate defect mechanisms, identify recurring issues, and analyse their impacts for a system portfolio of 64 MWp. A root cause analysis identified the failure pattern through material diagnostics of several power modules from inverters previously installed in the field. Prolonged exposure to high temperatures led to the degradation of the IGBT semiconductors, resulting in a breakthrough due to the short-term release of excessive heat. In parallel, an impact analysis was carried out based on historical monitoring data, that identified a faulty control behaviour of the inverter during curtailment. Due to the sharp increase in curtailment occurrences, a correlation of this observation was noted across nearly the entire portfolio. Finally, the study explored whether this randomly observed fault pattern that led to the inverter failure could have been detected in the data without prior knowledge of it. To achieve this, a method combining an artificial neural network and density-based clustering was proposed to automatically detect this recurring and propagating error pattern. This process was carried out in three steps: predicting the normal behaviour of the inverter, distinguishing between normal behaviour and anomalous behaviour, and differentiating the anomalous behaviour. The fault patterns were clearly assigned to four clusters. By introducing a scalable, data-driven fault diagnostics method, this study highlights how advanced materials science and data analytics can improve early fault detection and maintenance in PV portfolio monitoring, while also providing a deeper understanding of defect mechanisms. These combined approaches ultimately enhance inverter reliability and operational efficiency.

Accurate quantification of long-term Performance Loss Rate in photovoltaic systems is critical for ensuring system reliability, financial forecasting, and asset management across the global PV fleet. Conventional methods for estimating the performance loss rate, however, are often constrained by their sensitivity to environmental variability and reliance on rigid filtering heuristics that can introduce bias. This paper introduces a novel, data-driven framework that transcends these challenges by integrating unsupervised filtering, predictive modeling, and advanced trend analysis. The methodology employs Density-Based Spatial Clustering of Applications with Noise to adaptively isolate anomalous operational data while preserving approximately 80% of the core performance data. Subsequently, a Light Gradient Boosting Machine model, trained on early-life system data, establishes a weather-normalized performance baseline to generate a Performance Ratio Index—a high-fidelity time-series signal representing the system's intrinsic health. Finally, the degradation pathway is characterized via Seasonal-Trend decomposition combined with the Pruned Exact Linear Time algorithm, which robustly identifies change points and non-linear aging phases. The framework was validated across 8 distinct locations comprising 84 inverters, including commercial fleets and authoritative public benchmark datasets from Eurac Research and the FOSS Research Centre. While the broad fleet analysis captured a wide distribution of trend estimates (−4%/year to +3%/year) reflecting the method's sensitivity to data duration and sensor quality, the detailed primary case study demonstrated the framework's high precision, in identifying non-linear, multi-phase degradation. This analysis revealed complex aging dynamics that differed by device, including sharp initial deceleration and instances of mid-life performance acceleration. The resulting degradation rates, with both phase-specific and time-weighted averages ranging from −0.78%/year to −0.20%/year, were found to be physically plausible and consistent with reported industry benchmarks. These findings confirm the framework's utility as a scalable tool for automated performance loss rate assessment that separates non-linear degradation trends from environmental noise.