Search Results (1,151)

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

Accurate identification of solar photovoltaic model parameters is crucial for reliably representing electrical behavior, improving maximum power point tracking, and enhancing overall system performance. Owing to the nonlinear and multimodal nature of the single-diode model, analytical closed-form solutions are difficult to obtain, which necessitates the use of advanced optimization techniques. Metaheuristic methods are particularly suitable for this task due to their strong global search capability, independence from gradient information, and adaptability to complex solution landscapes. In this study, a hybrid metaheuristic approach called the Jackal Arithmetic Algorithm is evaluated by integrating the Arithmetic Optimization Algorithm with the Golden Jackal Optimization method. The optimization framework combines arithmetic-based operators to enhance global exploration with adaptive predatory-inspired strategies to strengthen local exploitation, enabling a smooth transition between exploration and exploitation and resulting in improved convergence stability. Simulation results confirm that the Jackal Arithmetic Algorithm provides highly accurate parameter estimation for the single-diode photovoltaic model, achieving a minimum root mean square error of 0.00078 with a population size of 70, outperforming all compared algorithms. Overall, the combined method offers a robust and effective solution for photovoltaic modeling, with direct benefits for system design, control, and real-time monitoring. Full article

To address the issues of poor geometric dimension retention, short component lifespan, and heavy maintenance workload of the 1520 mm gauge 50 kg/m rail No.9 turnout, a new design was proposed for the 1520 mm gauge 60 kg/m rail No.9 turnout. Based on the new design’s plane alignment, structural features, and other requirements, dynamic models of the vehicle–turnout system, the turnout conversion model, and the continuous welded rail turnout (CWR turnout) model were established. The focus was on analyzing the dynamic response of the vehicle when passing through the 1520 mm gauge 60 kg/m rail No.9 turnout, as well as its switching performance. The feasibility of applying CWR technology to this turnout was also explored. The results indicate that the dynamic indicators of the vehicle passing through the 1520 mm gauge 60 kg/m rail No.9 turnout meet the regulatory requirements; the maximum switching force at the traction point is 1.807 kN, which is less than the rated power of the switch machine; and the rail strength and track stability of the CWR turnout model all meet the design specifications. Full article

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty. Full article

This article describes the design, control, and implementation of a photovoltaic (PV) emulator using two-switch forward-converter topology. The system is designed to emulate the nonlinear electrical behavior of an actual PV panel under different environmental conditions including radiation level and temperature. The emulator provides galvanic isolation and also accurate current modulation to provide a safe yet reliable means of testing PV-related devices and algorithms within a laboratory setting. A dual-loop PI control is proposed to adjust the output current according to voltage feedback (VF), thus making accurate I–V and P–V curves achievable. Besides software simulation, a tailored printed circuit board (PCB) was fabricated. The simulation result demonstrated that the system can achieve a fast response and stable operation, with a maximum error percentage of about 2.1%, indicating high emulation fidelity, thereby providing an attractive platform for various evaluation purposes such as MPPT algorithms, inverters, and EMS. 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

Thermoelectric systems configured in multistring arrays of thermoelectric generators (TEGs) represent a promising solution for energy harvesting in environments with non-uniform thermal gradients. However, the presence of multiple maximum power points (MPPs) in such configurations poses significant challenges to energy extraction efficiency. This study presents a comprehensive performance evaluation of four maximum power point tracking (MPPT) algorithms, Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), applied to multistring thermoelectric generator (TEG) arrays under multiple and asymmetric thermal gradients. The simulated systems, modeled in MATLAB/Simulink, replicate real-world thermoelectric configurations by employing series-parallel topologies and eleven distinct thermal scenarios, including uniform, localized, and sinusoidal temperature distributions. The key contribution of this work lies in demonstrating the superior capability of metaheuristic algorithms (PSO and GA) to locate the global maximum power point (GMPP) in complex thermal environments, outperforming classical methods (P&O and InC), which consistently converged to local maxima under multi-peak conditions. Notably, PSO achieved the best average convergence time (0.23 s), while the GA recorded the fastest response (0.05 s) in the most challenging multi-peak scenarios. Both maintained high tracking accuracy (error ≈ 0.01%) and minimized power ripple, resulting in conversion efficiencies exceeding 97%. The study emphasizes the crucial role of algorithm selection in maximizing energy harvesting performance in practical TEG applications such as embedded systems, waste heat recovery, and autonomous sensor networks. Future directions include physical validation through prototypes, incorporation of dynamic thermal modeling, and development of hybrid or AI-enhanced MPPT strategies. Full article

Various maximum power point tracking (MPPT) techniques have been proposed to optimize the efficiency of solar photovoltaic (PV) systems. These techniques differ in several aspects such as design simplicity, convergence speed, implementation types (analog or digital), decision optimal point accuracy, effectiveness range, hardware costs, and algorithmic modes. Choosing the most suitable MPPT controller is crucial in PV system design, as it directly impacts the overall cost of PV solar modules. This paper presents a comprehensive exploration of 64 MPPT techniques for PV solar systems, covering optimization, traditional, intelligent, and hybrid methodologies. A comparative analysis of these techniques, considering cost, tracking speed, and system stability, indicates that hybrid approaches exhibit higher efficiency albeit with increased complexity and cost. Amidst the existing PV system review literature, this paper serves as an updated comprehensive reference for researchers involved in MPPT PV solar system design. Full article

Perovskite solar cells (PSCs) based on metal halides have garnered significant attention due to their exceptional power conversion efficiency (PCE) and compatibility with low-temperature fabrication processes. However, the development of stable and inexpensive carbon electrodes remains hindered by issues such as insufficient conductivity at the carbon electrode/perovskite interface and weak coupling strength. In this study, we employed a functionalized carbazole–cellulose composite (C–Cz) as an alternative binder to construct highly stable carbon electrodes for PSCs. The incorporation of C–Cz enhances electron interactions through its conjugated carbazole moieties, while the cellulose backbone facilitates uniform dispersion of carbon particles and forms continuous transport pathways. These synergistic effects significantly optimize interfacial energy alignment and defect passivation. Ultimately, p-i-n PSCs fabricated with C–Cz carbon paste electrodes achieved a champion PCE of 16.79%, substantially outperforming the control device using a conventional PMMA binder (10.56%). Notably, the exceptional hydrophobicity and defect passivation capabilities of the C–Cz electrode substantially enhance device durability—maintaining over 95% of initial efficiency after 400 h of continuous maximum power point tracking irradiation. This study reveals an effective adhesive engineering strategy for robust, scalable carbon electrodes, paving new pathways for practical applications in stable perovskite photovoltaics. Full article

Unmanned aerial vehicles (UAVs) are increasingly utilized across civilian and defense sectors due to their versatility, efficiency, and cost-effectiveness. However, their operational endurance remains constrained by limited onboard energy storage. Recent research has focused on electric propulsion systems integrated with hybrid energy sources, particularly the combination of solar cells and advanced battery technologies to overcome this limitation. This review presents a comprehensive analysis of the latest advancements in electric propulsion architecture, solar-based power integration, and hybrid energy management strategies for UAVs. Key components, including motors, electronic speed controllers (ESCs), propellers, and energy storage systems, are examined alongside emerging technologies such as wireless charging and flexible photovoltaic (PV) materials. Power management techniques, including maximum power point tracking (MPPT) and intelligent energy control algorithms, are also discussed in the context of long-endurance missions. Challenges related to energy density, weight constraints, environmental adaptability, and component integration are highlighted, with insights into potential solutions and future directions. The findings of this review aim to guide the development of efficient, sustainable, and high-endurance UAV platforms leveraging electric-solar hybrid propulsion systems. Full article

This work focuses on the development of a diagnostic approach for detecting and localizing sensor faults in an autonomous photovoltaic system. The considered system is composed of a photovoltaic module and a resistive load. However, an adaptation stage formed by a DC/DC voltage boost converter is necessary to transfer energy from the source to the load. The diagnostic scheme is based on a sliding mode observer (SMO) that is robust to uncertainties and parametric variations. The SMO incorporates adaptive gains optimized via parametric adaptation laws, with stability rigorously verified through Lyapunov analysis. The method effectively identifies both independent and simultaneous sensor faults, employing an optimized threshold selection strategy to balance detection sensitivity and false alarm resistance. Simulation results under varying environmental conditions, system parameter fluctuations, and noisy measurement demonstrate the approach’s superior performance, achieving a 20% reduction in mean absolute percentage error (MAPE) and 90% faster settling time compared to existing techniques. These enhancements immediately increase the dependability, efficiency, and lifetime of the PV system, which are critical for lowering carbon emissions and ensuring the economic feasibility of solar energy investments. Key innovations include a novel residual generation mechanism, seamless integration with backstepping sliding mode maximum power point tracking (MPPT) control, and enhanced transient response characteristics. Full article

This study presents the development and experimental validation of a novel wind turbine emulator (WTE) based on a doubly fed induction generator (DFIG). The proposed architecture employs an induction motor (IM) driven by a variable frequency drive (VFD) to emulate wind turbine dynamics, offering a cost-effective and low-maintenance alternative to traditional DC motor-based systems. The contribution of this work lies, therefore, not in the hardware topology itself, but in the complete real-time software implementation of the control system using C language and RTLib, which enables higher sampling rates, faster PWM updates, and improved execution reliability compared with standard Simulink/RTI approaches. The proposed control structure integrates tip–speed ratio (TSR)-based maximum power point tracking (MPPT) with flux-oriented vector control of the DFIG, fully coded in C to provide optimized real-time performance. Experimental results confirm the emulator’s ability to accurately replicate real wind turbine behavior under varying wind conditions. The test bench demonstrates fast dynamic response, with rotor currents settling in 11–18 ms, and active/reactive powers stabilizing within 25–30 ms. Overshoots remain below 10%, and steady-state errors are limited to ±1 A for currents and ±100 W/±50 VAR for powers, ensuring precise power regulation. The speed tracking error is approximately 0.61 rad/s, validating the system’s ability to follow dynamic references with high accuracy. Additionally, effective decoupling between active and reactive loops is achieved, with minimal cross-coupling during step changes. 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

In grid-connected photovoltaic systems, improving power quality is necessary for assuring constant energy delivery, consistent voltages, and current, as well as being compliant with the standards of the grid. Yet, today’s PV control systems have to deal with serious problems, for example, slow MPPT reactions to changes in irradiation, significant harmonic distortion, weak reaction to voltage changes, and being unable to adapt well to different situations. For this reason, these problems lead to less efficient electricity, unstable connections to the power grid, and an altered quality of electricity, as solar power and load levels vary in real conditions. A way to solve these problems is introduced in this paper: (1) the Hippopotamus-based Solar Power MPPT Tracker and (2) a SyBel embedded controller for controlling the inverter. This kind of optimization mimics nature to control the duty cycle and enables the boost converter to deliver maximum power while responding quickly and maintaining accurate tracking. Meanwhile, the SyBel controller makes use of a hybrid technique by using SNN, DBN, and synergetic logic to sensibly manage the inverter switches and increase the power quality. The framework is novel because it uses biological optimization plus deep learning-based embedded control to instantly handle error reduction and harmonic suppression. The whole process records energy from solar panels, follows the maximum power point, changes its schedule as needed, and uses sophisticated controls in the inverter. We found that the proposed MPPT tracker achieves an impressive tracking efficiency of 98.6%, surpassing PSO, FLC, and ANFIS, and lowering the time required for tracking by 72%. The SyBel inverter controller provides outstanding results, keeping the voltage THD at 1.2% and current THD at 1.3%, which matches power quality standards. Full article