Search Results (295)

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

The rapid expansion of China’s photovoltaic (PV) industry has led to a significant increase in decommissioned PV modules. To address the high energy consumption and environmental impact of traditional recycling techniques, this study proposes a novel method that integrates DMPU solvent recycling with pyrolysis for recovering PV cell sheets. DMPU, an organic solvent with low volatility, non-toxicity, and excellent recyclability, was used in this study. The effects of temperature and treatment duration on the structural integrity of silicon cell sheets were systematically evaluated, establishing optimal parameters: immersion in DMPU at 200 °C for 60 min, followed by pyrolysis at 480 °C for 60 min. A case study was conducted on a small-scale recycling facility with a daily processing capacity of 200 standard PV panels, encompassing system boundaries such as transportation, pretreatment, and pyrolysis. The recycling process consumed 2.14 × 10⁹ kJ of energy annually, reducing CO₂ emissions by 9357.2 tons. Compared to conventional methods such as pyrolysis, mechanical dismantling, and chemical dissolution, the proposed approach employing a green, recyclable solvent markedly reduces energy consumption and carbon emissions, offering notable environmental benefits. Full article

This paper evaluates the efficacy of the Black-Winged Kite Algorithm (BKA) for parameter estimation in single-, double-, and triple-diode photovoltaic (PV) models. This study targets key electrical parameters, including photocurrent, reverse saturation current, series, and shunt resistances, and diode ideality factor(s) using experimental I-V data from an RTC France silicon cell. Performance is assessed using the root mean square error (RMSE) and convergence behavior and benchmarked against established metaheuristics including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO). The results show that BKA achieves competitive RMSE values with stable convergence for the investigated dataset. BKA employs coupled exploration and exploitation updates inspired by hunting and migration behaviors, and its limited number of control parameters supports straightforward deployment in nonlinear PV identification tasks. The results support BKA as a viable optimization option for PV model fitting in this setting, while also reflecting the typical trade-offs between search diversity and computational effort inherent to population-based methods. Full article

The increasing frequency and intensity of heat waves across Southern Europe pose growing challenges to the performance and profitability of photovoltaic (PV) systems. This study quantifies the impact of elevated ambient temperatures on three large-scale PV power plants located in distinct Portuguese climatic zones: Amareleja, Alcoutim, and Tábua. Using 15 years of hourly meteorological data from PVGIS (2009–2023), five temperature models—NOCT, Faiman, PVSyst, NOCT (SAM), and Sandia—were implemented to estimate cell temperature and corresponding PV output under reference and elevated temperature conditions (+2 °C and +5 °C). A three-fold sensitivity analysis assessed (i) the influence of module parameters (temperature coefficient and NOCT), (ii) the effect of stochastic, non-uniform temperature perturbations mimicking realistic heat waves, and (iii) the impact of the selected PV performance model by comparing the simplified linear temperature-corrected approach with the one-diode and three-parameter (1D + 3P) model. Results show that a uniform +2 °C rise reduces annual energy yield by 0.74% and a +5 °C rise by 1.85%, while stochastic perturbations slightly amplify these losses to 0.80% and 2.01%. The 1D + 3P model predicts stronger nonlinear effects, with reductions of −2.42% and −6.06%. Although modest at plant scale, such impacts could translate into annual national revenue losses exceeding 10 million EUR, considering Portugal’s 6.32 GW installed PV capacity. The findings highlight the importance of accounting for realistic temperature dynamics and model uncertainty when assessing PV performance under a warming climate. Full article

This article presents the design of a perovskite photovoltaic (PV)-based power management system, which uses a power converter (a four-stage bootstrap charge pump) to boost the output of the solar cell and supply selectable rectified power rails to CMOS image sensor circuit blocks. A perovskite photovoltaic, also known as a perovskite solar cell (PSC) was fabricated in the laboratory. The PSC has an open-circuit voltage of 1.14 V, short-circuit current of 1.24 mA, maximum power of 0.88 mW, and a current density of 20.68 mA/cm² at 62% fill factor. These measured forward scan parameters were closely reproduced with a solar cell simulation model. In a Cadence simulation that used 180 nm CMOS process, the power converter efficiently boosts the maximum output voltage of the PSC from 0.85 V to a rectified 3.7 V. Stage modulation and level shifting enable selectable output rails in the 1.2–3.3 V range to supply the image sensor circuit blocks. Keeping the output capacitance of the power converter much larger than the flying capacitance reduces the ripple voltage to approximately 73 µV, much smaller than the typical 1 mV in several other literatures. Through simulation, this work demonstrates the concept of directly using PSC (to be implemented on an outer ‘packaging’, not on a die) to supply CMOS image sensor power rails, in the same sense as in wearable devices and other consumer devices. This work highlights a path toward self-powered image sensors with improved conversion efficiency, compactness, and adaptability in low-light and variable operating environments. Full article

Unmanned Aerial Vehicles (UAVs) equipped with thermal and RGB cameras and enhanced by deep learning offer a powerful solution for autonomous photovoltaic (PV) system inspection. However, defect detection performance depends on flight parameters such as altitude, camera angles, speed, and solar position. This study examines the impact of various UAV flight parameters on the accurate detection of critical PV defects including hotspots, dirt from bird droppings, dust accumulation, and cell failures. For this purpose, two datasets were developed, comprising over 38,000 thermal infrared and RGB images. Using the YOLOv11 model, 21 flight configurations varying in altitude, camera tilt and pan angles, speed, and solar position were evaluated at four different times of day to assess the combined ambient and geometric effects on detection accuracy. Results indicate that low-altitude flights enhance small-object detection, while higher altitudes improve coverage at the expense of fine-detail accuracy. Dust detection is most effective when the camera aligns with the sun, whereas steep midday tilts cause reflective false positives. Thermal defect detection performs best during morning flights with moderate tilt angles. These findings emphasize the need to balance accuracy, coverage, efficiency, and safety, offering practical guidelines for effective and scalable PV inspection and maintenance. Full article

This study focuses on first-generation crystalline silicon photovoltaic (PV) cells, which remain the core of the PV industry. It outlines the structure and operation of single-junction cells, distinguishing between monocrystalline and polycrystalline technologies. A literature review was conducted using databases such as Web of Science and Scopus to identify research trends and inform future research directions. PV cell classification by generation is also presented based on production methods and materials. The experimental section includes both electrical and structural characterisation of crystalline silicon solar cells, with particular emphasis on the influence of the number and geometry of front-side busbars on metal-semiconductor contact resistance and electrical properties. Additionally, the paper highlights the use of dedicated laboratory equipment—such as a solar simulator (for determining photovoltaic cell parameters from current-voltage characteristics) and Corescan equipment (for determining layer parameters using the single-tip probe method)—in evaluating PV cell properties. This equipment is part of the Photovoltaics and Electrical Properties Laboratory at the Silesian University of Technology. The findings demonstrate clear structural correlations that can contribute to optimising the performance and longevity of silicon-based PV cells. Full article

To reduce the amount of harmful gases produced by fossil fuels, more environmentally friendly and sustainable alternatives are being proposed around the world. As a result, technologies for manufacturing hydrogen fuel cells and producing green hydrogen are becoming more widespread, with an impact on energy production and environmental protection. In many countries around the world, and in Africa in particular, leaders, scientists, and populations are considering switching from fossil fuels to so-called green energies. Hydrogen is therefore an interesting alternative that deserves to be explored, especially since both rural and urban populations have shown an interest in using it in the near future, which would reduce pollution and the proliferation of greenhouse gases, thereby mitigating global warming. The aim of this paper is to determine the hybrid energy system best suited to addressing the energy problem in the study area, and then to make successive substitutions of different energy sources, starting with the most polluting, in order to assess the possibilities for transitioning the energy used in the area to green hydrogen. To this end, this study began with a technical and economic analysis which, based on climatic parameters, led to the proposal of a PV/DG-BATTery system configuration, with a Net Present Cost (NPC) of USD 19,267 and an average Cost Of Energy (COE) of USD 0.4, and with a high proportion of CO₂ emissions compared with the PV/H₂GEN-BATT and H₂GEN systems. The results of replacing fossil fuel generators with hydrogen generators are beneficial in terms of environmental protection and lead to a reduction in energy-related expenses of around 2.1 times the cost of diesel and a reduction in mass of around 2.7 times the mass of diesel. The integration of H₂GEN, at high duty percentages, increases the Cost Of Energy, whether in a hybrid PV/H₂GEN system or an H₂GEN system. This shows the interest in the study country in using favorable duty proportions to make the use of hydrogen profitable. Full article

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

The present study analyses various mathematical models from the technical literature for calculating photovoltaic cell temperature, emphasizing wind velocity as a key parameter. Since cell temperature significantly affects photovoltaic module efficiency, researchers are actively pursuing simple and cost-effective cooling methods for these systems. First, the study surveys existing mathematical models for computing cell temperature and evaluates how model parameters affect calculations. Second, it demonstrates computational outcomes using selected formulae—chosen based on criteria outlined in the paper—to predict PV cell temperatures under varying wind conditions using meteorological data from Bucharest, Romania. The analysis employs a transient mathematical model based on a single ordinary differential equation, validated against experimental data from previous studies. The results reveal circumstances where alternative mathematical approaches produce similar outcomes, alongside situations where substantial discrepancies emerge. The investigation concludes by contrasting computational forecasts against empirical observations, providing valuable guidance for future research in this domain. Full article

Accurate parameter estimation of solar cells is critical for early-stage fault diagnosis in photovoltaic (PV) power systems. A physical model based on three-diode configuration has been recently introduced to improve model accuracy. However, nonlinear and recursive relationships between internal parameters and PV output, along with parameter drift and PV degradation due to long-term operation, pose significant challenges. To address these issues, this study proposes a deep learning-based ensemble framework that integrates outputs from multiple optimization algorithms to improve estimation precision and robustness. The proposed method consists of three stages. First, the collected data were preprocessed using some data processing techniques. Second, a PV power generation system is modeled using the three-diode structure. Third, several optimization algorithms with distinct search behaviors are employed to produce diverse estimations. Finally, a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to learn from these results. Experimental validation on a 733 kW PV power generation system demonstrates that the proposed method outperforms individual optimization approaches in terms of prediction accuracy and stability. Full article

As photovoltaic (PV) deployment has expanded from rural sites to the built environment, rooftops are increasingly used for electricity generation. In these settings, the visible sky is often partially obstructed by adjacent walls, producing shading that reduces energy yield. This study quantifies the effect of wall shading on incident solar radiation and system losses, and contrasts it with inter-row (mutual) shading experienced by PV arrays in open fields. Systems installed near obscuring walls are subject to both phenomena. To our knowledge, the specific impact of wall shading on PV systems has not been examined comprehensively. We characterize how wall height governs shadow geometry, determine the resulting numbers of shaded and unshaded cells and modules, and assess how shaded modules influence the performance of the remaining modules in a series string. For the parameter set analyzed, annual energy losses are 7.7% due to wall shading and 4% due to inter-row shading, yielding a combined loss of 10.2%. The methods and results provide a practical basis for designers to estimate shading losses and expected energy production for PV systems sited near obscuring walls. Full article

Bifacial photovoltaic (PV) modules have the advantage of using light reflected off of the ground to contribute to power production. Predicting the energy gain is challenging and requires complex models to do so accurately. Often, module degradation over time is neglected in models for the sake of simplicity or is underestimated. Comparing outdoor and indoor current–voltage (I–V) performance for bifacial modules is more challenging than for monofacial modules, as there are additional variables to consider such as rear albedo non-uniformity, cell mismatch, and their effects on temperature. This challenge is compounded when heterogeneous degradation modes occur, such as polarization-type potential-induced degradation (PID-p). To examine the effects of PID-p on I–V predictions using an empirical data-driven approach, 16 bifacial PERC modules are installed outdoors on racks with different albedo conditions. A subset is exposed to high-voltage biases of −1500 V or +1500 V. Outdoor data are traced at irradiance ranges of 150–250 W/m², 500–600 W/m², and 900–1000 W/m². These curves are corrected using control module temperature, wire resistivity, and module resistance measured indoors. We examine several methods to transform indoor I–V curves to accurately, and more simply than existing methods, approximate outdoor performance for bifacial modules without and with varying levels of PID-p degradation. This way, bifacial performance modeling can be more accessible and informed by fielded, degraded modules. Distributions of percent errors between indoor and outdoor performance parameters and Mean Absolute Percent Errors (MAPEs) are used to assess method quality. Results including low-irradiance data (150–250 W/m²) are discussed but are filtered for quantifying method quality as these data introduce substantial errors. The method with the most optimal tradeoff between low MAPE and analysis simplicity involves measuring the front side of a module indoors at an irradiance equal to plane-of-array irradiance plus the product of module bifaciality and albedo irradiance. This method gives MAPE values of 1–6.5% for non-degraded and 1.6–5.9% for PID-p degraded module performance. Full article

Although the exponential increase in photovoltaic installations does contribute to mitigating climate change, it has posed the problem of photovoltaic (PV) residue. As PV panels contain strategic metals, their recovery has become a priority. This paper therefore employs a mesoporous carbon impregnated with P507 extractant as adsorbent to selectively recover gallium and indium from solutions simulating the leachate of end-of-life CIGS (Copper Indium Gallium Selenide) cells in a fixed-bed. The previous batch results obtained in our lab show that both metals can be selectively separated by simply adjusting the initial pH, with large adsorption capacities (44.97 mg/g for gallium and 34.24 mg/g for indium). The obtained breakthrough curves were fitted to the Thomas, Yan, Yoon, and HSDM (Homogeneous Surface Diffusion Model) models using a simulation program developed in Python 3.12 obtaining good results in all cases (R² > 0.9). The estimated parameters were used to predict the experimental breakthrough curve for a different experiment that had not been used for parameter estimation, being the best predictive results the obtained with the HSDM. This is logical, given that unlike the other three models, it is mechanistic. Full article

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article