Search Results (497)

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems. Full article

In a PV array, mismatch among modules changes the shape of the P–V curve and may create several power peaks. This makes maximum power point tracking (MPPT) more difficult, especially when the tracker needs to distinguish the global maximum power point (GMPP) from local peaks. This paper studies this problem with SVM-ILEA, a hybrid MPPT method that combines support vector machine (SVM) regression and an improved love evolution algorithm (ILEA). The SVM model takes module irradiance and temperature as inputs and predicts a voltage close to the GMPP. ILEA uses this voltage as the search center and avoids scanning the full voltage range. The modified convergence factor and adaptive distance factor further adjust the voltage movement during iteration, giving wider search steps at the early stage and smaller corrections near the optimum to reduce steady-state power oscillations. The simulation setup in MATLAB/Simulink R2019b includes standard test conditions (STC) and static partial shading with non-uniform irradiance and temperature distributions, as well as dynamic operating conditions. Across the four static conditions, SVM-ILEA achieves mean tracking times of 0.0233–0.0303 s and mean steady-state power fluctuations of 0.0111–0.0500 W. Across the three dynamic tests, the mean MPPT efficiency ranges from 97.9057% to 98.2991%. The results obtained demonstrate fast GMPP tracking, small power fluctuation, and stable re-tracking under complex PV operating conditions. Full article

Background/Objectives: Myocardial bridging is a common anatomic variant in which an epicardial coronary artery takes an intramyocardial course. A true intracameral, or intracavitary, coronary course is rare. On coronary CT angiography (CTA), multiplanar and curved planar reformations answer most of these questions, but some cases stay equivocal because of partial-volume effects, cardiac motion, and loss of depth cues. A vessel next to a thin or fat-infiltrated wall can also mimic an intracameral course. Cinematic rendering (CR) is a photorealistic three-dimensional post-processing method that uses Monte Carlo path tracing with global illumination to reproduce natural depth and soft-tissue shading. Methods: In this retrospective, single-center, four-patient case series, we describe patients in whom CR was used alongside standard two-dimensional and reformatted CTA after the planar reconstructions had been read as equivocal. The cases were purposively selected as illustrative examples rather than consecutively enrolled, and the report is intended to be hypothesis-generating. Results: In two patients with left anterior descending (LAD) myocardial bridging, CR showed the tunneled segment with depth and was useful for discussion with referring clinicians and for teaching. In two patients whose coronary segment looked intracameral on planar images, CR showed intact overlying myocardium, which favored an intramyocardial course over chamber entry. Conclusions: In these selected patients, CR did not add anatomic information that was unavailable on expert reformations. It appeared to improve perceived depth and reader confidence in these equivocal cases and was useful for communication and teaching; no quantitative image analysis or formal reader study was performed. Because this is a small, retrospective case series without a comparison group, these observations are illustrative and hypothesis-generating and cannot be extrapolated to coronary CTA in general; prospective comparative studies are needed to determine whether CR adds diagnostic value over standard reconstructions. Full article

This paper presents an experimental comparative study of four advanced maximum power point tracking (MPPT) strategies—backstepping, adaptive backstepping, sliding mode control, and vision-based MPPT—combined with three wireless communication technologies: IEEE 802.15.4, Wi-Fi, and 3G. The comparison is performed on a distributed photovoltaic (PV) platform under common power-stage conditions in harmonized irradiance scenarios, including abrupt uniform-irradiance transients and controlled partial shading patterns. Under uniform irradiance, adaptive backstepping achieved the best overall dynamic behavior, with the shortest average convergence time of 0.045 s using IEEE 802.15.4, compared with 0.052 s for Wi-Fi and 0.115 s for 3G, while all tested configurations maintained tracking efficiencies above 98.8%. Under partial shading, the ranking changed substantially: the vision-based MPPT provided the best GMPP-oriented performance, reaching the highest tracking success rate and the lowest energy loss. In particular, its lost energy increased from 0.20% with IEEE 802.15.4 to 0.45% with 3G, whereas conventional backstepping increased from 0.65% to 1.35% over the same communication range. Latency measurements showed that IEEE 802.15.4 exhibited the lowest median end-to-end delay and the smallest dispersion, Wi-Fi showed intermediate behavior, and 3G introduced the largest latency and variability. The results demonstrate that MPPT performance in distributed PV systems depends on both the control strategy and the communication architecture and that a unified experimental assessment of both layers is required to identify the optimal MPPT–communication combination for each operating scenario. Full article

Increasing planting density is an effective strategy for improving maize (Zea mays L.) productivity, but it can also intensify interplant competition and canopy shading. Enhanced nitrogen application may help offset these negative effects. A two-year field experiment was conducted in the Hexi Corridor, an arid region of northwestern China, using a full factorial design with three planting density levels D1 (75,000 plants ha⁻¹), D2 (90,000 plants ha⁻¹), and D3 (105,000 plants ha⁻¹), and three nitrogen application levels N1 (198 kg ha⁻¹), N2 (264 kg ha⁻¹), and N3 (330 kg ha⁻¹). The aim was to clarify how the interaction between planting density and nitrogen application regulates maize canopy structure and affects resource use efficiency in arid areas. The results showed that planting density, nitrogen rate, and their interaction significantly affected canopy structure, photosynthetic traits, grain yield, and water and nitrogen use efficiency. From the perspective of each growth stage, combinations of medium and high planting density and nitrogen application levels facilitated the optimization of maize canopy structure, promoted plant growth and dry matter accumulation, and elevated leaf SPAD values. Meanwhile, treatment D2N2 exhibited the most prominent improvement in maize yield components, with grain yield increased by 1.44–35.58% on average across experimental years. This treatment also sustained superior water and nitrogen use efficiency, achieving an average water use efficiency of 3.53 kg·m⁻³ and an average partial factor productivity of nitrogen of 53.41 kg·kg⁻¹. Comprehensive multi-index evaluation verified that D2N2 represented the optimal cultivation regime. This regime could reduce nitrogen fertilizer input by 20% while fully exploiting light and heat resources inherent to arid regions. Collectively, this study establishes a viable green and high-efficiency cultivation paradigm for maize production with high yield, reduced fertilizer input and water conservation, and delivers critical theoretical and technical references for the sustainable intensification of maize cultivation in arid regions of northwest China. Full article

To reveal the degradation mechanism and the formation mechanism of multiple power peaks in photovoltaic modules under irregular shading conditions, this study investigated the performance of full-cell photovoltaic modules through a combination of experimental and simulation approaches. First, the I–V and P–V characteristics of photovoltaic modules under partial and complete shading of photovoltaic cells at different locations were analyzed. The results show that, under local partial shading conditions, the photovoltaic module characteristics are mainly determined by the shaded area, whereas under complete cell shading conditions, the performance degradation primarily depends on the number of bypassed cell strings. A single-diode photovoltaic module simulation model was developed, and the coupling effects of representative partial shading ratios and the number of participating cell strings were analyzed on the MATLAB/Simulink platform. The results indicate that the characteristics of photovoltaic modules under partial shading conditions are jointly governed by the coupling effects of shading ratio and cell-string participation. As the shaded area increases, the degree of electrical mismatch among cells becomes more pronounced, causing the P–V characteristics to evolve from a single-peak profile to a multi-peak profile, accompanied by reduced peak differences and increasingly blurred characteristic boundaries. Through a comprehensive analysis of the simulation and experimental results, it is found that complex shading conditions enhance the multi-peak characteristics of power while causing the peak values to converge. These accompanying features increase the likelihood of multi-peak power points, thereby posing greater challenges to inverter operation and maximum power point tracking techniques in photovoltaic arrays. It is of great significance for optimizing the configuration of photovoltaic arrays and designing high-precision maximum power point tracking strategies. Full article

Partial shading is one of the main factors that degrade the output performance and operational reliability of photovoltaic (PV) arrays. It not only causes power loss and multi-peak P–V characteristics, but also induces current mismatch, reverse bias, and local hotspot formation. In this study, an electro-thermal PV module model under partial shading conditions is developed and validated, and an improved sparrow search algorithm (ISSA) is proposed for maximum power point tracking (MPPT) of PV arrays under static and dynamic complex operating conditions. The electrical model is established based on the single-diode model with irradiance, temperature, and Bishop reverse bias corrections, while the thermal model considers solar absorption, heat generation, convection, radiation, and heat conduction. The coupled model is validated against published experimental and numerical results. The predicted peak hotspot temperature is 111.9 °C, corresponding to a relative error of 2.7%; the average absolute errors of current and voltage are 0.20–0.25 A and approximately 0.3 V, respectively, and the maximum relative error of peak temperature is 3.7%. Based on the validated model, a MATLAB/Simulink MPPT platform is constructed to compare particle swarm optimization (PSO), the standard sparrow search algorithm (SSA), and the proposed ISSA. The results show that SSA achieves better global tracking performance than PSO under severe partial shading and dynamic irradiance transitions. Furthermore, by introducing Tent chaotic initialization and random walk perturbation, ISSA significantly improves the convergence speed and reduces steady-state power fluctuation while maintaining high tracking efficiency. Under static shading conditions, ISSA reduces the convergence time from 0.44 s to 0.25 s, 0.24 s to 0.15 s, and 0.44 s to 0.26 s for light, moderate, and severe shading cases, respectively. Under dynamic conditions, ISSA also shortens the post-transition convergence time and suppresses output power oscillation. These results demonstrate that the proposed ISSA-based MPPT method is suitable for PV arrays operating under partial shading and dynamic weather conditions. Full article

The goal of agrivoltaic engineers is to use growing space for the synergistic production of both food and energy, typically via photovoltaic (PV) capture. Most research in this area has been carried out in arid, high-light environments, but subtropical and temperate regions are also critical production zones, and installation designs vary considerably. In this study, tomato and lettuce production using an agrivoltaic high tunnel (HT) design specific for a subtropical environment (NE Georgia, USA, USDA Zone 8A) was tested using organic production standards. The design utilized typical HTs (approx. 11 m × 5 m) with solar panel arrays hung internally. The design aimed to (1) meet off-grid power needs, (2) mitigate excessive temperature and humidity, (3) balance shade and plant productivity, and (4) simplify installation and maintenance. Treatments were replicated at the HT level, and cultivar differences were assessed to identify genotypes that might serve in future work to optimize yield under partial shade. In 2023 and 2024, we employed novel organic photovoltaic (OPV) panels, which are partially opaque. The OPV panels provided sufficient energy needs to maintain beneficial conditions without external power sources. In 2024, tomato plants in the OPV HTs experienced an area-weighted daily light integral (DLI, mol photons m⁻² d⁻¹) of approximately 31.8 (95% CI [28.9, 34.7]), compared to 34.7 (95% CI [31.8, 37.6]) in non-OPV HTs, an approximate reduction of 8%. Average maximum temperatures in the OPV HTs were 33.5 °C (95% CI [30.6, 36.4], compared to 35.1 °C (95% CI [30.9, 39.2]) in the non-OPV HTs, an approximate reduction of 1.6 °C. In 2023, tomato marketable yield was reduced by approximately 0.9 kg per plant in OPV HTs compared to non-OPV HTs (p = 0.023). In 2024, yields were statistically equivalent across all treatments (p > 0.1), while marketable fraction was improved relative to 2023 and was greatest in the HTs. Lettuce yield for both years was unaffected by the presence of HTs or OPV panels (p > 0.1). In 2025, we conducted an additional experiment using a shade-equivalent array of conventional 100% opaque photovoltaic (PV) panels and observed a similar reduction in DLI and no significant impact on tomato yield parameters (p > 0.1 Both designs were effective at equilibrating conditions inside the HTs to ambient temperature levels outside the tunnels. Using results from the study, an app for agrivoltaic value estimation was developed. Based on that software, the presented agrivoltaic design under currently available silicon–PV technology achieves an 18% annual return, assuming system depreciation is minimal and surplus energy could be applied to other on-farm needs. Full article

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Maximum power point tracking (MPPT) is essential for improving the energy harvesting performance of grid-connected photovoltaic systems under varying operating conditions. However, the growing diversity of MPPT algorithms has made the selection of suitable control strategies increasingly challenging for researchers. This review presents a comprehensive analysis of MPPT techniques for grid-connected photovoltaic systems, with particular emphasis on dynamic environmental variations, partial shading conditions, and grid-interfacing requirements. The study systematically classifies and evaluates conventional methods, intelligent control approaches, and bio-inspired optimization techniques. In contrast to earlier review articles that mainly emphasize traditional methods such as Perturb and Observe and Incremental Conductance, this work focuses on two distinctive aspects: the comparative literature compilation of modern artificial intelligence and metaheuristic-based MPPT algorithms; and the inclusion of power quality considerations in MPPT performance evaluation. Quantitative assessment metrics derived from various experimental conditions are aggregated to provide a broader comparison of control strategies. In addition, the impact of MPPT methods on power quality parameters, particularly total harmonic distortion and power factor, is examined. The review further summarizes recent advances in metaheuristic optimization for challenging operating scenarios and identifies key research gaps. Finally, practical guidelines are provided for selecting and developing MPPT strategies for residential, commercial, and utility-scale photovoltaic applications, with particular attention to sensorless and grid-aware control solutions for future power networks. Full article

Curved photovoltaic (PV) systems provide greater architectural form adaptability for building-integrated photovoltaic (BIPV) applications. However, the combined effects of external shading and self-shading result in degradation in power output. In this work, the effectiveness of static reconfiguration techniques for performance optimization of curved BIPV systems under complex partial shading conditions was comparatively evaluated. Employing a 6 × 6 total-cross-tied (TCT) curved PV system with a 120° central angle as the case study, this work simulated the curved irradiance distribution and the corresponding I–V/P–V characteristics through an experimentally proven simulation model. A comparative investigation was performed to evaluate the performance enhancement achieved by three static reconfiguration strategies under the complex combined self-shading and external shading conditions. The results indicate that, compared with the original TCT topology without reconfiguration, the proposed static reconfiguration strategies increased the maximum power output up to 58% by effectively mitigating current mismatch under complex shading conditions. Different static reconfiguration strategies exhibit differentiated advantages when addressing specific shading patterns. Overall, static reconfiguration is demonstrated to be a viable optimization approach for curved BIPV systems without introducing additional electrical complexity, and the selection of specific strategies should be determined by the local shading conditions. Full article

The increasing importance of photovoltaic (PV) systems in the context of the energy transition, together with the need to improve their efficiency, has driven the adoption and development of intelligent and advanced maximum power point tracking (MPPT) techniques. Among these approaches, the Particle Swarm Optimization (PSO) algorithm stands out due to its simplicity, ease of implementation, low number of control parameters, robustness, and fast convergence capability, making it widely applied in modern MPPT systems. However, the performance of PSO in MPPT applications depends on the appropriate selection of both algorithm control parameters and implementation/configurations parameters. The control parameters include the cognitive (C₁) and social (C₂) learning factors, as well as the inertia factor (w), which directly influence swarm dynamics and the balance between exploration and exploitation mechanisms, that is, between global and local search. On the other hand, configuration parameters such as the number of particles and the initialization strategy affect the initial population diversity, the convergence speed toward the maximum power point, and the computational cost of the algorithm, defining the trade-off between speed and accuracy. Despite the extensive research in this field, there is still no clear consensus regarding the most suitable PSO parameter configuration for MPPT applications. This paper presents a statistical analysis of PSO parameter selection in MPPT applications, identifying the most frequently adopted parameter configurations and trends reported in the literature. The findings provide useful guidelines for researchers to select the PSO parameters according to different operating conditions, particularly under partial shading and irradiance variations. From a sustainability perspective, improving MPPT performance contributes to maximizing PV energy harvesting, reducing energy losses, and enhancing the reliability of PV systems, thereby supporting the transition toward more sustainable energy generation. Full article

The first yellow-leafed crabapple variety developed in China is Malus ‘Duojiao’. The light level affects its leaf color. (1) Background: Plants are frequently shaded by photovoltaic panels and green buildings. It is unknown how genetic regulation and partial shadowing regulate leaf color. (2) Methods: Four 28-day shading treatments were used for ‘Duojiao’ crabapple and its maternal ‘Xifu’ crabapple. Virus-induced gene silencing (VIGS), overexpression transgenic validation experiments, and physiological index analysis were employed to identify the expression levels of significant candidate genes. (3) Results: Improvements in chlorophyll synthesis, mineral metabolism, and antioxidant status were observed. The net photosynthetic rate was 39.29% higher under double-layer shade than in the control. (4) Conclusions: Partial double-layer shading exhibited the optimal effect. MsCPOX was the key gene controlling leaf color. Our results provide a theoretical basis for analyzing light responses and determining genes regulating leaf color in crabapple. Full article

Crop yield responses to reduced solar radiation are central to the design of agrivoltaic systems, yet crop-specific patterns and critical shading thresholds remain insufficiently characterized across diverse environments. This study evaluates yield responses across a global dataset of 546 observations from 66 studies, including agrivoltaic, shading, and agroforestry systems. Relative yield was analyzed in relation to reduction in solar radiation (RSR), crop type, and environmental variables using exploratory analysis, multiple linear regression, and tree-based ensemble models. Crop responses varied systematically across crop types. Fruits, berries, and fruity vegetables maintained or increased yield under lower shading levels, while forages, leafy vegetables, cereals, and tubers showed gradual declines, and maize and grain legumes exhibited the strongest sensitivity. Across models, yield responses were non-linear, with relatively stable yields at lower shading levels followed by accelerated declines beyond approximately 50–60% RSR. Climatic conditions further influenced these patterns, with crops in higher-radiation and warmer environments maintaining yields more effectively under partial shade. These findings demonstrate that crop yield responses depend on crop type, shading intensity, and environmental context, providing an agronomic basis for crop selection and agrivoltaic system design. Full article