Search Results (458)

The growing global population has increased the demand for reliable security systems, especially in areas with limited or unstable energy infrastructure. Renewable energy sources, particularly solar panels, offer an effective solution to ensure continuous operation of cameras and sensors on security poles in such regions. This study analyzes data from a solar-powered security pole and develops Maximum Power Point Tracking (MPPT) algorithms to improve system efficiency. The original design, which relied solely on a buck converter, lacked flexibility. To address this, a buck–boost converter capable of operating in both buck and boost modes was designed, and the proposed algorithms were implemented and tested on this converter. Classical MPPT techniques, including Perturb and Observe (P&O) and Incremental Conductance (IC), were evaluated for their performance. Additionally, under partial shading conditions, metaheuristic approaches such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) were examined and compared. The performance of all algorithms was assessed in terms of energy efficiency and system adaptability. This study aims to contribute to renewable energy-based solutions by developing flexible and high-performance energy management systems for applications with limited energy access, such as security poles in rural areas. Full article

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

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems. Full article

The global shift toward sustainable energy systems has led to an increased adoption of PV systems, driven by their enhanced performance and environmental benefits, including reduced carbon emissions. Improving the efficiency of Grid-Tied Photovoltaic Systems (GTPVS) is essential for guaranteeing reliable and sustainable renewable power integration. This research paper presents advanced hybrid Maximum Power Point Tracking (MPPT) designed for GTPVS to maximize PV energy harvesting and support grid sustainability. The proposed technique combines Advanced Variable Step Size Incremental Conductance (AVIC) for reference voltage generation and an Integral Backstepping Control (IBC) to regulate the control of the step-up converter. This hybrid technique enables rapid convergence speed, reduces power losses, and enhances stability under fast-changing environmental conditions, Partial Shading Conditions (PSCs), and grid disturbances conditions. This MPPT is evaluated via the MATLAB/Simulink environment, version 2020b, and validated in real time using a Processor-in-the-Loop (PIL) setup on the eZdsp TMS320F28335 platform. Comparative analysis with benchmark methods confirms its superiority, with an average tracking performance of 99.57%, a response time of 0.02 s, and a Total Harmonic Distortion (THD) of 0.69%, accompanied by negligible steady-state oscillations. These findings indicate the validity and sustainability of the AVIC-IBC MPPT for real-time GTPVS operating under realistic climatic conditions. Full article

The 2025 International Technology Roadmap for Photovoltaics (ITRPV) projects that bifacial modules will dominate the photovoltaic (PV) market, reaching roughly 60–80% global share between 2024 and 2035, while monofacial PV modules will steadily decline. Current industry practice is to route the cable bundles along structural members such as main beams or torque tubes, thereby preventing rear-side shading but resulting in two key drawbacks: increased cable length and decreased system reliability due to cable proximity with rotating members and pinch points. Both effects contribute to higher system costs and reduced cable reliability. An alternative method involves suspending cable bundles directly behind the modules using hangers. While this approach mitigates excess length and risk of cable snags, it introduces the possibility of partial rear-side shading, which could possibly cause performance loss and hot-spot formation due to shade-induced electrical mismatch. Experimental evidence indicates that this risk is minimal, as albedo irradiance typically represents only 10–30% of front-side irradiance as reported in the literature and is largely diffuse, thereby limiting the likelihood of significant directional shading. This study evaluates the performance and reliability impacts of hanger-supported cable bundles under varying experimental conditions. Performance metrics assessed include maximum power output (Pmax), short-circuit current (Isc), open-circuit voltage (Voc), and fill factor (FF), while hot-spot risk was evaluated through measurements of module temperature uniformity using infrared imaging. Each cable (1X) was 6 AWG with a total outer diameter of approximately 9 mm. Experiments covered different cable bundle counts/sizes (2X, 6X, 16X), mounting configurations (fixed-tilt and single-axis tracker), and albedo conditions (snow-covered and snow-free ground). Measurements were conducted hourly on clear days between 8:00 and 16:00 from June to September 2025. The results consistently show that hanger-supported cable bundles have a negligible shading impact across all hours of the day and throughout the measurement period. This indicates that rear-side cable shading can be safely and practically disregarded in performance modeling and energy-yield assessments for the tested configurations, including fixed-tilt systems and single-axis trackers with or without torque tube shading and with various hanger sizes and cable-bundle counts. Therefore, hanging cables behind modules is a cost- and reliability-friendly, safe and recommended practice. Full article

Optimizing energy conversion in photovoltaic (PV) systems is crucial for maximizing energy conversion efficiency and ensuring reliable operation. Achieving this requires that the PV array consistently operates at the Global Maximum Power Point (GMPP). Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O) and Incremental Conductance (INC), perform effectively under uniform irradiance but fail to track the GMPP under partial shading conditions (PSCs), resulting in energy losses and degraded system efficiency. To overcome this limitation, this paper proposes a hybrid MPPT method that integrates the Crayfish Optimization Algorithm (COA), a bio-inspired metaheuristic, with the P&O technique. The proposed approach combines the global exploration ability of COA with the fast convergence of P&O to ensure accurate and stable GMPP identification. The algorithm is validated under multiple irradiance patterns and benchmarked against established MPPT methods, including voltage-source and current-source region detection, Improved Variable Step Perturb and Observe and Global Scanning (VSPO&GS), and a hybrid Particle Swarm Optimization (PSO)-P&O method. Simulation studies performed in MATLAB/Simulink demonstrate that the proposed technique achieves higher accuracy, faster convergence, and enhanced robustness under PSCs. Results show that the proposed method reliably identifies the global peak, limits steady-state oscillations to below 1%, restricts maximum overshoot to 0.5%, and achieves the fastest settling time, stabilizing at the new power point significantly faster following major step changes, thereby enhancing overall PV system performance. Full article

This paper proposes a hybrid photovoltaic (PV) Maximum Power Point Tracking (MPPT) strategy to tackle local optima, slow dynamic response, and steady-state oscillations under partial shading conditions (PSC). The method combines an Improved Whale Migration Algorithm (IWMA) with a variable-step Incremental Conductance (VINC) technique. IWMA employs a Tent–Logistic–Cosine chaotic initialization, dynamic weight coefficients, random feedback, and a distance-sensitive term to enhance population diversity, strengthen global exploration, and reduce the risk of convergence to local maxima. The VINC stage adaptively adjusts the step size based on incremental conductance, providing fine local refinement around the global maximum power point (GMPP) and suppressing steady-state power ripple. Extensive MATLAB/Simulink simulations with multiple random trials show that the proposed IWMA-VINC strategy consistently outperforms the Whale Migration Algorithm (WMA), A Simplified Particle Swarm Optimization Algorithm Combining Natural Selection and Conductivity Incremental Approach (NSNPSO-INC), and the Grey Wolf Optimizer and Whale Optimization Algorithm (GWO-WOA) under both static and dynamic PSC, achieving the highest tracking accuracies (99.74% static, 99.44% dynamic), higher average output power, shorter convergence times, and the smallest variance across trials. These results demonstrate that IWMA-VINC offers a robust and high-performance MPPT solution for PV systems operating in complex illumination environments. Full article

The urgent pursuit of climate-resilient agriculture and clean energy systems, central to the Energy–Agriculture Nexus and the UN Sustainable Development Goals, has accelerated global interest in agrivoltaic (Agri-PV) technologies. This paper presents a global systematic review and meta-analysis of 160 peer-reviewed studies, structured through a five-stage thematic synthesis: (1) mapping global and regional Agri-PV deployment and potential, (2) analyzing system design and modeling methodologies, (3) evaluating crop suitability under partial shading, (4) reviewing enabling policies and regulatory frameworks, and (5) assessing techno-economic feasibility and investment barriers. Results reveal that Europe and Asia lead Agri-PV development, driven by incentive-based policies and national tenders, while limited regulatory clarity and high capital costs constrain wider adoption. Despite technological progress, no integrated model fully captures the coupled energy, water, and crop dynamics essential for holistic assessment. Strengthening economic valuation, policy coherence, and standardized modeling approaches will be critical to scale Agri-PV systems as a cornerstone of sustainable and climate-resilient land use. Full article

Although the rare plant Cotoneaster integerrimus is distributed across Eurasia, ecological information on its isolated populations at the easternmost range limit in Korea has been entirely lacking. This study was conducted to (1) characterize the environmental characteristics of the habitat of Korean C. integerrimus populations and (2) predict potential habitats via a simple species distribution model (SDM) based on ridge logistic regression and presence–background data, providing a foundation for effective conservation strategies. To this end, we analyzed habitat type, topography, and light conditions through field surveys and combined these data with an SDM fitted to six known occurrences on limestone ridges. Results revealed a clear ecological divergence; the Korean population is biased toward partial shade and north-facing slopes within the forest understory, in contrast to European populations inhabiting open, rocky sites. This distribution pattern is interpreted as a local adaptive strategy that reduces exposure to hot and humid summer conditions. Furthermore, a unique morphological trait not reported in European populations was identified: dense persistent hairs that remain until seed maturity. The SDM analysis showed moderate discrimination (training AUC = 0.784) and indicated that high elevation and ridge topography (Topographical Position Index, TPI) acted as key habitat factors, whereas annual mean temperature was the strongest limiting factor. Mapping the upper decile (top 10%) of predicted suitability within the limestone belt highlighted a small, spatially restricted set of high-elevation ridges as candidate microrefugia and survey priorities. This study suggests that the Korean C. integerrimus population may have undergone local adaptation due to isolation. Furthermore, this population is considered both a Geographical Peripheral Population (GPP) and a glacial relict, and is assessed to be vulnerable to climate change. Given that the SDM is based on only six occurrences and shows variable performance among spatial folds, all spatial predictions and variable effects should be regarded as exploratory and spatially conservative rather than as definitive habitat projections. These findings, therefore, support the urgent need to establish in situ and ex situ conservation strategies that preserve this geographically peripheral population as an irreplaceable component of the species’ genetic diversity. Full article

Maximum power point tracking (MPPT) under partial shading is a nonconvex, rapidly varying control problem that challenges multi-agent policies deployed on photovoltaic modules. We present Fuzzy–MAT3D, a fuzzy-augmented multi-agent TD3 (Twin-Delayed Deep Deterministic Policy Gradient) controller trained under centralized training/decentralized execution (CTDE). On the theory side, we prove that differentiable fuzzy partitions of unity endow the actor–critic maps with global Lipschitz regularity, reduce temporal-difference target variance, enlarge the input-to-state stability (ISS) margin, and yield a global $L_{\infty }$$\gamma$-contraction of fixed-policy evaluation (hence, non-expansive with $\kappa =\gamma <1$). We further state a two-time-scale convergence theorem for CTDE-TD3 with fuzzy features; a PL/last-layer-linear corollary implies point convergence and uniqueness of critics. We bound the projected Bellman residual with the correct contraction factor (for $L^{\infty }$ and $L^2\left(\rho \right)$ under measure invariance) and quantified the negative bias induced by $min\{Q_1,Q_2\}$; an N-agent extension is provided. Empirically, a balanced common-random-numbers design across seven scenarios and 20 seeds, analyzed by ANOVA and CRN-paired tests, shows that Fuzzy–MAT3D attains the highest mean MPPT efficiency (92.0% ± 4.0%), outperforming MAT3D and Multi-Agent Deep Deterministic Policy Gradient controller (MADDPG). Overall, fuzzy regularization yields higher efficiency, suppresses steady-state oscillations, and stabilizes learning dynamics, supporting the use of structured, physics-compatible features in multi-agent MPPT controllers. At the level of PV plants, such gains under partial shading translate into higher effective capacity factors and smoother renewable generation without additional hardware. Full article

Early detection of performance degradation and prevention of critical failures in photovoltaic (PV) arrays are essential for ensuring system reliability and efficiency. This study presents an intelligent fault detection and classification framework based on a Multi-Layer Neural Network (MLNN). The model was developed and validated using a simulated 250 kW grid-connected PV system tested under five operating scenarios: normal operation, open-circuit fault, partial short-circuit, partial shading, and string-to-string fault. Unlike conventional diagnostic approaches, the proposed model directly processes raw electrical measurements (current, voltage, power, irradiance, and temperature) under varying environmental conditions, thus emulating real-world operational variability. The MLNN achieved 98% test accuracy and outperformed benchmark classifiers Support Vector Machine (SVM) and Random Forest (RF) across multiple metrics. Performance was evaluated using the confusion matrix, precision, recall (sensitivity) and F1-score. The framework is designed for scalability and can be integrated into predictive maintenance platforms to enable early fault detection and improve long-term PV system availability and efficiency. Full article

Aim: This in vitro study aimed to evaluate the marginal and internal fit of three monolithic CAD/CAM zirconia ceramics with different Y-TZP contents, prepared with chamfer and rounded shoulder finish lines. Methods. Sixty monolithic zirconia crowns were fabricated and divided into three groups (n = 20) based on their yttria content: (1) multilayer zirconia consisting of a dentin layer of 3 mol% yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) and an incisal layer of 5 mol% partially stabilized zirconia (5Y-PSZ), (2) shade-gradient zirconia fully composed of 3Y-TZP, and (3) shade-gradient zirconia containing 4 mol% partially stabilized zirconia (4Y-PSZ). Each group was further divided into two finish line configurations (chamfer and rounded shoulder). Marginal and internal gaps were measured using the silicone replica technique under a stereomicroscope by a single operator. Data were analyzed using three-way ANOVA followed by Tukey’s post hoc test (α = 0.05). Marginal and internal gaps were assessed using the silicone replica technique under a stereomicroscope by a single operator. Statistical analysis was performed with three-way ANOVA and Tukey’s post hoc test (p < 0.05). Results: The occlusal region exhibited the largest gap values, while the axial region showed the smallest across all groups. Mean marginal and internal gaps were 33.79 µm for chamfer and 43.37 µm for rounded shoulder finish lines. Zirconia with higher Y-TZP content demonstrated significantly greater gap values than those with lower percentages (p < 0.05). Significant interactions were found among finish line design, material type, and measurement region (p < 0.05), with rounded shoulder margins showing larger gaps (p = 0.001). Conclusions: Y-TZP content significantly affects marginal and internal adaptation, with higher percentages associated with increased gap values. Both finish line types produced clinically acceptable fits, although chamfer margins provided superior adaptation. Full article

This paper describes the modeling, simulation, and experimental validation of a Hybrid supercapacitor–battery Energy Storage System (HESS) for photovoltaic (PV) modules under partial shading. The system is intended to provide an uninterruptible power supply for a DC primary load. The Hybrid Power System (HPS) architecture includes a DC/DC boost converter with a Maximum Power Point Tracking (MPPT) algorithm that optimizes photovoltaic (PV) energy extraction. Furthermore, two bidirectional DC–DC converters are dedicated to the battery and supercapacitor subsystems to allow the bidirectional power flow within the HPS. The proposed HESS is evaluated through MATLAB/Simulink simulations and experimentally validated on a prototype using real-time hardware based on the dSPACE DS1104. To optimize power flow within the HPS, two energy management strategies are implemented: the Thermostat-Based Method (TBM) and the Filter-Based Method (FBM). The results indicate that the thermostat-based strategy provides better battery protection under shading conditions. Indeed, with this approach, the battery can remain in standby for 300 s under total permanent shading (100%), and for up to 30 min under dynamic partial shading, thereby reducing battery stress and extending its lifetime. Full article

Maximum power point tracking (MPPT) control is a key technology for increasing the power generation of photovoltaic arrays under varying light and temperature conditions. Traditional perturb and observe methods and incremental conductance methods can achieve good tracking performance for single-peak characteristics. However, under complex conditions such as partial shading or dust accumulation, the power-voltage curve of a photovoltaic array exhibits multi-peak characteristics. In such cases, traditional methods may get trapped in local optima, preventing the photovoltaic array from operating at the maximum power point. Swarm intelligence algorithms perform well when solving multi-extremum functions and can be used for MPPT control of photovoltaic arrays in complex environments. Therefore, this paper focuses on the fireworks algorithm (FWA). To improve the computational speed and global optimization capability of the FWA, the characteristics of each stage of the algorithm are analyzed, a comprehensive improved fireworks algorithm (CIFWA) is proposed, and it is applied to the MPPT control of photovoltaic systems. The improved algorithm introduces an adaptive resource allocation and selection strategy with community inheritance features and applies tent chaos mapping to the algorithm’s explosion behavior. Multiple sets of test functions are used to compare the performance metrics of the optimization algorithm, demonstrating improvements in computational speed and global search capability of CIFWA. Finally, a control strategy for the MPPT of photovoltaic arrays based on CIFWA is presented, and a simulation experimental platform is built to analyze and verify the control performance. Full article

The integration of large-scale photovoltaic (PV) systems requires advanced converter architectures capable of ensuring both high efficiency and fast dynamic response. Leveraging the inherent modularity and low harmonic distortion of Modular Multilevel Converters (MMCs), this paper presents a novel control and modulation framework for grid-connected PV applications. The key innovation lies in the implementation of distributed, string-level Maximum Power Point Tracking (MPPT), enabling optimal energy extraction even under non-uniform (shaded) irradiance conditions. The proposed method operates within a dual time-scale control architecture: an outer Perturb and Observe (P&O) loop assigns independent power references, while the inner modulation stage employs an innovative switching strategy that activates only one module per sampling period. Unlike conventional MPPT-based schemes, where submodules are driven by voltage references, the proposed approach directly regulates the power of each MMC submodule, eliminating the need for PV-side current measurement. Full article