Search Results (846)

Surface defects of photovoltaic (PV) cells can seriously affect power generation efficiency. Accurately detecting such defects and handling them in a timely manner can effectively improve power generation efficiency. Aiming at the high-precision and real-time requirements for surface defect detection during the use of PV cells, this paper proposes a PV cell surface defect detection algorithm based on SEC-YOLOv8. The algorithm first replaces the Spatial Pyramid Pooling Fast module with the SPPELAN pooling module to reduce channel calculations between convolutions. Second, an ECA attention mechanism is added to enable the model to pay more attention to feature extraction in defect areas and avoid target detection interference from complex environments. Finally, the upsampling operator CARAFE is introduced in the Neck part to solve the problem of scale mismatch and enhance detection performance. Experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 69.2% on the PV cell dataset, which is 2.6% higher than the original network, which is designed to achieve a superior balance between the competing demands of accuracy and computational efficiency for PV defect detection. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

This study presents preliminary findings from an experimental campaign conducted on a pilot-scale green hydrogen production plant powered by a photovoltaic (PV) system. The integrated setup, implemented at the University “Mediterranea” of Reggio Calabria, includes renewable energy generation, hydrogen production via electrolysis, on-site storage, and reconversion through fuel cells. The investigation assessed system performance under different configurations (on-grid and selective stand-alone modes), focusing on key operational phases such as inerting, purging, pressurization, hydrogen generation, and depressurization. Results indicate a strong linear correlation between the electrolyser’s power setpoint and the pressure rise rate, with a maximum gradient of 0.236 bar/min observed at 75% power input. The system demonstrated robust and stable operation, efficient control of shutdown sequences, and effective integration with PV input. These outcomes support the technical feasibility of small-scale hydrogen systems driven by renewables and offer valuable reference data for calibration models and future optimization strategies. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

Improving photovoltaic (PV) panel performance under extreme climatic conditions is critical for advancing sustainable energy systems. In hyper-arid regions, elevated operating temperatures significantly reduce panel efficiency. This study investigates and compares three cooling techniques—air cooling, water cooling, and porous media cooling—using thermal and electrical modeling based on CFD simulations in ANSYS. The numerical model replicates a PV system operating under peak solar irradiance (900 W/m²) and realistic ambient conditions in Adrar, Algeria. Simulation results show that air cooling leads to a modest temperature reduction of 6 °C and a marginal efficiency gain of 0.25%. Water cooling, employing a top-down laminar flow, reduces cell temperature by over 35 °C and improves net electrical output by 30.9%, despite pump energy consumption. Porous media cooling, leveraging passive evaporation through gravel, decreases panel temperature by around 30 °C and achieves a net output gain of 26.3%. Mesh sensitivity and validation against experimental data support the accuracy of the model. These findings highlight the significant potential of water and porous material cooling strategies to enhance PV performance in hyper-arid environments. The study also demonstrates that porous media can deliver high thermal effectiveness with minimal energy input, making it a suitable low-cost option for off-grid applications. Future work will integrate long-term climate data, real diffuser geometries, and experimental validation to further refine these models. Full article

One of the methods for mitigating the duck curve phenomenon in photovoltaic (PV) energy systems is storing surplus energy in the form of hydrogen. However, there is a lack of studies focused on residential PV systems that assess the impact of hydrogen storage on the reduction of energy flow imbalance to and from the national grid. This study presents an analysis of hydrogen energy storage based on real-world data from a household PV installation. Using simulation methods grounded in actual electricity consumption and hourly PV production data, the research identified the storage requirements, including the required operating hours and the capacity of the hydrogen tank. The analysis was based on a 1 kW electrolyzer and a fuel cell, representing the smallest and most basic commercially available units, and included a sensitivity analysis. At the household level—represented by a single-family home with an annual energy consumption and PV production of approximately 4–5 MWh over a two-year period—hydrogen storage enabled the production of 49.8 kg and 44.6 kg of hydrogen in the first and second years, respectively. This corresponded to the use of 3303 kWh of PV-generated electricity and an increase in self-consumption from 30% to 64%. Hydrogen storage helped to smooth out peak energy flows from the PV system, decreasing the imbalance from 5.73 kWh to 4.42 kWh. However, while it greatly improves self-consumption, its capacity to mitigate power flow imbalance further is constrained; substantial improvements would necessitate a much larger electrolyzer proportional in size to the PV system’s output. Full article

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes. Full article

This study presents the design and techno-economic comparison of two standalone photovoltaic (PV) systems, each supplying a 1 kW critical load with 100% reliability under Cairo’s climatic conditions. These systems are modeled for both the constant and the night load scenarios, accounting for the worst-case weather conditions involving 3.5 consecutive cloudy days. The primary comparison focuses on traditional lead-acid battery storage versus green hydrogen storage via electrolysis, compression, and fuel cell reconversion. Both the configurations are simulated using a Python-based tool that calculates hourly energy balance, component sizing, and economic performance over a 21-year project lifetime. The results show that the PV/H₂ system significantly outperforms the PV/lead-acid battery system in both the cost and the reliability. For the constant load, the Levelized Cost of Electricity (LCOE) drops from 0.52 USD/kWh to 0.23 USD/kWh (a 56% reduction), and the payback period is shortened from 16 to 7 years. For the night load, the LCOE improves from 0.67 to 0.36 USD/kWh (a 46% reduction). A supplementary cost analysis using lithium-ion batteries was also conducted. While Li-ion improves the economics compared to lead-acid (LCOE of 0.41 USD/kWh for the constant load and 0.49 USD/kWh for the night load), this represents a 21% and a 27% reduction, respectively. However, the green hydrogen system remains the most cost-effective and scalable storage solution for achieving 100% reliability in critical off-grid applications. These findings highlight the potential of green hydrogen as a sustainable and economically viable energy storage pathway, capable of reducing energy costs while ensuring long-term resilience. Full article

Due to the high demand for fuel efficiency, electric vehicles have come into the picture, as they only use batteries to power the vehicle. This requires constant charging of the batteries at charging stations, which are costly and impractical to install. But it is possible to install charging stations by making use of photovoltaic (PV) cells and demagnetization currents to self-charge batteries under stand-still conditions. The design of a bidirectional converter with asymmetrical half-bridge converter based on a switched reluctance motor (SRM) drive, using PV for electric vehicles, is implemented in this paper. It consists of developing a control unit (GCU), Li-ion battery pack, and photovoltaic (PV) solar cells that are integrated with a bidirectional converter and asymmetrical half-bridge converter (AHBC) to provide power to the SRM drive. The solar-assisted SRM drive can be operated in either the motoring mode or charging mode. In the motoring-mode GCU, the battery or PV energy can be used in any combination to power the SRM. In the charging-mode PV, the GCU and AC grids are used to charge the battery under stand-still conditions. This work helps in the self-charging of batteries using either the GCU or PV cells, as well as aids in the improvement in the performance characteristics. Also, this work compares the performance metrics for the proposed system and conventional system. The performance of the drive system using PV cells/GCU is evaluated and verified through MatLab/Simulink and experimental results. Full article

Concentrating photovoltaic-thermal (CPVT) collectors use reflective surfaces to focus sunlight onto a smaller receiver area, increasing thermal energy output while maintaining annual energy efficiency. Ray-tracing simulations are employed in this study using Tonatiuh to optimise the characteristics of the Double MaReCo (DM) collector, which is an improved version of the commercially available Solarus Power Collector (PC). Focused on enhancing electrical performance, the photovoltaic (PV) cell configurations are varied on the bottom side of the receiver, while the top-side PV cells remain constant. The study also analyses the influence of diodes and transparent gables on the annual solar irradiance received by the PV cells. From the analysis, it is observed that the specific annual irradiance received by the PV cells in the DM collector with transparent gables is nearly 64% more compared to that of the PC counterpart. It is also observed that the transparency of gables becomes significant only when the whole area of the receiver is covered by PV cells. With the goal of improving performance while lowering the cost and complexity of the DM collector, the study investigates various collector design characteristics that may shed more light on optimising the current model. Full article

The amount of energy consumed worldwide is raising at a startling rate. This has led to a global energy crisis and a hike in fuel prices and has caused environmental jeopardy. Renewable energy resources offer a promising solution to the above situation. Solar energy is examined to be the most liberal source of renewable energy. The efficiency of solar PV cells show nonlinear characteristics and deliver poor performance. Consequently, it is imperative to use the maximum power point tracking (MPPT) technique to extract the optimum amount of energy from photovoltaic (PV) cells. Perturb and Observe (P&O) and Incremental Conductance (INC) are examples of MPPT algorithms. The performance of MPPT schemes below varying climatic ambience should be predominantly considered. The workings of these schemes under various load conditions becomes critical to analyze. This work deals with this issue and compares the conventional P&O MPPT and INC MPPT schemes for various solar irradiation and load conditions and designing solar panels optimized for maximum power generation. The designed MPPT scheme is carried out in the control circuit of a boost converter, evaluating and designing a converter to convert solar panel DC power into grid-compatible AC power. By analyzing different methods for managing and tracking PV power, this method proves to be fast and gives better results under changes in solar insolation. Full article

Saudi Arabia’s 2030 Vision plans to install 40 GW of photovoltaic capacity in the country by 2030. This includes a requirement that deployed systems achieve a local content threshold of 33–35% for 2024–25, increasing to 40–45% for 2028 and beyond. With the exception of financing (75%), the level of local content for all other aspects of PV farms in Saudi Arabia is low (22–50%). In this paper, we consider the domestic manufacturing capacities of key components such as float glass, aluminum framing, steel, and concrete. Capacity constraints are evident, importing PV cells rather than modules (to increase local content by undertaking module lamination in the country) would require 58% of Saudi Arabia’s float glass production from now until 2030. We estimate that 85% of modules will need to be manufactured in their entirety in country if the local content of all other aspects does not change. Such an approach could result in higher commodity prices in Saudi Arabia, certainly in the short term, leading to import sourcing and, in effect, worsening of local content of PV systems. Therefore, increasing local content across all aspects of PV systems is needed, with a focus on the local skills base and capacity. Full article

The flow velocity field of the oil-filled acrylic solar sphere is assessed using flow visualization, which includes image processing and Particle Image Velocimetry (PIV) measurements. The temperature, sphere size, and thickness all have an impact on the generated convection flow. The acrylic sphere, a contemporary concentrated photovoltaic technology, collects solar energy and concentrates it into a small focal region. This focus point is positioned precisely above a multi-junction apparatus that serves as an appliance for concentrator cells. Instead of producing the same amount of electricity as a typical photovoltaic panel (PV), this gadget can generate an enormous power rate directly. There are numerous industrial uses for acrylic spheres as well. This study paper aims to examine the flow properties inside a sphere and investigate the impact of the sphere’s temperature, size, and thickness on the fluid motion’s flow velocity. Furthermore, the goal of this research is to elucidate the correlation between these variables to enhance power-generating performance by achieving higher efficiency. The findings demonstrated that the flow structure value is greatly affected by the sphere size, thickness, and temperature. It is discovered that when the spherical thickness lowers, the velocity rises. As a result, the sphere performs better at lower liquid temperatures (35–40 °C), larger sizes (15–30 cm diameter), and reduced acrylic thickness (3–4 mm), leading to up to a 23% increase in power output and a 35–50% rise in internal flow velocity compared to thicker and smaller configurations. Therefore, reducing the sphere thickness by 1 mm results in approximately a 10% increase in average flow velocity at the top of the sphere, corresponding to an increase of about 0.0001 m/s. Notably, the sphere with a 3 mm thickness demonstrates superior power and efficiency compared to other thicknesses. As the sphere’s thickness decreases, the solar sphere’s output power and efficiency rise. The amount of sunlight absorbed by the acrylic photons increases with decreasing acrylic layer thickness; hence, the greater the output power, the higher the efficiency that follows. Full article

Despite the convenience of solar potential and the magnitude of energy received by the Earth from the sun, solar photovoltaic systems have failed to meet the growing energy demand. This can be attributed to various factors such as low cell efficiency, environmental conditions, and improper tracking of operating points, which further worsen the system’s performance. Various advanced metaheuristic-based Maximum Power Point Tracking (MPPT) techniques were reported in the literature. Most available techniques were designed and tested in subscription-based/paid software such as MATLAB/Simulink, PSIM simulator, etc. Due to this, the simulation and analysis of these MPPT algorithms for developing and underdeveloped countries added an extra economic burden. Many open-source PV libraries are computationally intensive, lack active support, and prove impractical for MPPT testing on resource-constrained hardware. Their complexity and absence of optimization for edge devices limit their viability for the edge device. This issue is addressed in this research by designing a robust framework using an open-source programming language i.e., Python. For demonstration purposes, we simulated and analyzed a solar PV system and benchmarked its performance against the JAP6 solar panel. We implemented multiple metaheuristic MPPT algorithms including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), evaluating their efficacy under both Standard Test Conditions (STC) and complex partial shading scenarios. The results obtained validate the feasibility of the implementation in Python. Therefore, this research provides a comprehensive framework that can be utilized to implement sophisticated designs in a cost-effective manner for developing and underdeveloped nations. Full article

Renewable energy sources are critical to the global effort to achieve carbon neutrality. Alongside hydropower, wind and nuclear plants, the photovoltaic (PV) systems developed greatly, with new PV technologies emerging in recent years. Although the conversion efficiencies are improving and the materials used have a lower impact on the environment, the feasibility of these technologies is required to be assessed. This paper proposes a levelized cost of energy (LCOE) model to assess the feasibility of five PV technologies: high-efficiency silicon heterojunction cells (HJT), N-type monocrystalline silicon cells (N-type), P-type passivated emitter and rear contact cells (PERC), N-type tunnel oxide passivated contact cells (TOPCon) and bifacial TOPCon. The LCOE considers capital investment, government incentives, operation and maintenance costs, residual value of PV modules and total energy output during the PV system’s life span. To determine the influence of PV system’s capacity over the LCOE values, three systems are analyzed for each technology: 3 kW, 5 kW and 7 kW. The results show that the largest PV systems have the lowest LCOE values, ranging from 2.39 c€/kWh (TOPCon) to 2.92 c€/kWh (HJT) when incentives are accessed, and ranging from 6.05 c€/kWh (TOPCon) to 6.51 c€/kWh (HJT) without subsidies. The 3 kW and 5 kW PV systems have higher LCOE values due to lower energy output during lifetime. Full article