Search Results (153)

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 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

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

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

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

This paper presents a reconfigurable switching circuit and control methodology for mitigating power losses in photovoltaic (PV) systems under partial shading. The proposed hardware uses a simplified network of power MOSFETs and diodes to enable dynamic reconfiguration between series and parallel connections, improving energy yield with minimal conduction losses. Unlike conventional approaches that require irradiance measurements or extensive sensing, the control algorithm uses only per-module voltage and a single-current measurement to detect shading events in real time. A novel switching strategy reduces the number of actively controlled transistors, simplifying the control circuitry and reducing power dissipation. Both simulation and experimental results validate the method. Simulations of a 4-module PV system showed maximum power point (MPP) increases from 900 W to over 1100 W and from 460 W to 900 W, with full recovery to 1500 W after shading removal. Experimental verification on a 3-module setup under controlled shading yielded similar improvements: MPP increased from 38.4 W to 45.6 W and from 38.4 W to 45.8 W. These results demonstrate rapid adaptability, effective mismatch loss reduction, and maximisation of available power, making the proposed design a practical and low-overhead solution for commercial PV systems with non-uniform irradiance. Full article

The main objective of this paper is to develop a maximum power point tracker (MPPT) for a photovoltaic module array (PVMA) under conditions of partial shading and sudden changes in solar irradiance. PVMAs exhibit nonlinear characteristics with respect to temperature and solar irradiance conditions. Therefore, when some modules in the array are shaded or when there is a sudden change in solar irradiance, the maximum power point (MPP) of the array will also change, and the power–voltage (P-V) characteristic curve may exhibit multiple peaks. Under such conditions, if the tracking algorithm employs a fixed step size, the time required to reach the MPP may be significantly prolonged, potentially causing the tracker to converge on a local maximum power point (LMPP). To address the issues mentioned above, this paper proposes a novel MPPT technique based on the nature-inspired idols algorithm (IA). The technique allows the promotion value (PM) to be adjusted through the anti-fans weight (afw) in the iteration formula, thereby achieving global maximum power point (GMPP) tracking for PVMAs. To verify the effectiveness of the proposed algorithm, a model of a 4-series–3-parallel PVMA was first established using MATLAB (2024b version) software under both non-shading and partial shading conditions. The voltage and current of the PVMAs were fed back, and the IA was then applied for GMPP tracking. The simulation results demonstrate that the IA proposed in this study outperforms existing MPPT techniques, such as particle swarm optimization (PSO), cat swarm optimization (CSO), and the bat algorithm (BA), in terms of tracking speed, dynamic response, and steady-state performance, especially when the array is subjected to varying shading ratios and sudden changes in solar irradiance. Full article

Partial shading poses a significant challenge to photovoltaic (PV) systems by degrading power output and overall efficiency, especially under non-uniform irradiance conditions. This paper proposes an advanced time-sharing maximum power point tracking (MPPT) control strategy implemented through a non-isolated single-phase multi-input microinverter architecture. The system enables individual power regulation for multiple PV modules while preserving their voltage–current (V–I) characteristics and eliminating the need for additional active switches. Building on the concept of distributed MPPT (DMPPT), a flexible full power processing (FPP) framework is introduced, wherein a single MPPT controller sequentially optimizes each module’s output. By leveraging the slow-varying nature of PV characteristics, the proposed algorithm updates control parameters every half-cycle of the AC output, significantly enhancing controller utilization and reducing system complexity and cost. The control strategy is validated through detailed simulations and experimental testing under dynamic partial shading scenarios. Results confirm that the proposed system maximizes power extraction, maintains voltage stability, and offers improved thermal performance, particularly through the integration of GaN power devices. Overall, the method presents a robust, cost-effective, and scalable solution for next-generation PV systems operating in variable environmental conditions. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. 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

Bypass diode faults are among the most hard-to-detect but impactful anomalies in photovoltaic (PV) systems, especially under partial shading conditions, where their electrical signatures often resemble those caused by non-critical irradiance variations. This study presents a systematic simulation-based investigation into how different bypass diode fault types—short-circuited, open-circuited, and healthy—affect the electrical behavior of PV strings under diverse irradiance profiles. A high-resolution MATLAB/Simulink model is developed to simulate 27 unique diode fault configurations across multiple shading scenarios, enabling the extraction of key features from resulting I–V curves. These features include global and local maximum power point parameters, open-circuit voltage, and short-circuit current. To address the challenge of feature redundancy and classification ambiguity, a preprocessing step is applied to remove near-duplicate instances and improve model generalization. An artificial neural network (ANN) model is then trained to classify the number of faulty bypass diodes based on these features. Comparative evaluations are conducted with support vector machines and random forests. The results indicate that the ANN achieves the highest test accuracy (93.57%) and average AUC (0.9925), outperforming other classifiers in both robustness and discriminative power. These findings highlight the importance of feature-informed, data-driven approaches for fault detection in PV systems and demonstrate the feasibility of diode fault classification without precise fault localization. Full article

Although the upper nodal roots are vital for resource uptake in late-stage maize, their anatomical and physiological responses to varying nitrogen and light regimes remain unclear. In a field experiment, maize was grown under three nitrogen levels (0, 192, and 240 kg·ha⁻¹) and two light regimes (normal light and 50% shading). At flowering (R1), we selected the number, diameter, anatomy of nodal roots, root-bleeding sap composition, and grain yield. Sample sizes ranged from three to twelve replicates per treatment, depending on the trait measured. Data were analyzed using ANOVA and Duncan’s test (p < 0.05). Under normal light, N₁₉₂ and N₂₄₀ significantly enhanced stele and vessel diameters in the sixth and seventh nodal root whorls, vessel number and cortical cell layers in the fifth and seventh whorls, root-bleeding intensity, exudation rates of sucrose, abscisic acid, key free amino acids (Asn, Asp, Glu), and grain yield, compared to N₀. Shading markedly suppressed the nodal root anatomical structure, reducing root-bleeding intensity by 18.2–26.6% and yield by 30.6–40.8%; especially under SS-N0, which also notably increased the exudation of stress-related amino acids (particularly Asp and Glu). Correlation analysis revealed positive relationships of root-bleeding intensity with vessel area and grain yield, indicating that impaired root anatomy restricts resource transport under shading. Sufficient nitrogen partially alleviated these adverse effects. This study demonstrates that light and nitrogen synergistically regulate the upper nodal root anatomy, thereby modulating root-bleeding sap and ultimately influencing grain yield. These results provide a theoretical basis for high-yield maize cultivation and precision nitrogen management under low-light stress. Full article

Partial shading condition (PSC) for photovoltaic (PV) arrays complicates the operation of PV systems at peak power due to the existence of multiple peak points on the power–voltage (P–V) characteristic curve. Identifying the global peak among multiple peaks presents challenges, as the system may become trapped at a local peak, potentially resulting in significant power loss. Power generation is reduced, and hot-spot issues might arise, which can cause shaded modules to fail, under the partly shaded case. In this paper, instead of focusing on local peaks, several effective, precise, and dependable maximum power point tracker (MPPT) systems monitor the global peak using a fuzzy logic controller. The suggested method can monitor the total of all PV array peaks using an interleaved boost converter DC/DC (IBC), not only the global peaks. A DC/DC class boost converter (CBC), the current gold standard for traditional control methods, is pitted against the suggested converter. Four PSC-PV systems employ three-phase inverters to connect their converters to the power grid. Full article

Photovoltaic (PV) power generation may vary with respect to several factors such as solar radiation, temperature, power conditioning units, environmental effects, and shading conditions. The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. The main reason for efficiency reduction under partial shading conditions is the creation of multiple local maximums and one global maximum operating point. The classical Maximum Power Point Tracking (MPPT) algorithm fails to determine the global maximum operating point to prevent power losses under partial shading conditions. In this study, a novel hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization that mainly aims to determine global operating point, is proposed. The proposed hybrid MPPT method is tested under different partial shading conditions and variable irradiance levels. In this manner, the dynamic response of the system is remarkably increased by the proposed MPPT method. To show the superiority of the developed method, a performance comparison is conducted with the P&O- and Kalman-Filter-based MPPT methods. The obtained results illustrate an improvement around 1.5 V in undershoot voltage and 0.2 ms in convergence speed. In addition, the overall system efficiency of the PV system is increased around 2% when compared to the P&O- and Kalman-Filter-based MPPT methods. Consequently, the proposed method seems to be an efficient method in terms of undershoot voltage, convergence time, tracking accuracy, and efficiency under partial shading conditions. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article