Search Results (247)

A multi-port conversion system that connects photovoltaic (PV) arrays, battery energy storage (BES), and an electric vehicle (EV) to a single-phase grid offers a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, optimizing usage, improving grid stability, and supplying backup power. The proposed four-port converter consists of an interleaved bidirectional DC-DC converter for high-voltage BES, a bidirectional buck–boost DC-DC converter for EV charging and discharging, a DC-DC boost converter with MPPT for PV, and a grid-tied inverter. Its non-isolated structure ensures high efficiency, compact design, and fewer switches, making it suitable for residential applications. A state-of-charge (SoC)-based power management strategy coordinates operation among PV, BES, and EV in both on-grid and off-grid modes. It reduces reliance on EV energy when supporting V2G and V2H, while SoC balancing between BES and EV extends lifetime and lowers current stress. A 7.5 kVA system was simulated in MATLAB/Simulink to validate feasibility. Two scenarios were studied: PV, BES, and EV with V2G supporting the grid and PV, BES, and EV with V2H providing backup power in off-grid mode. Tests under PV fluctuations and load variations confirmed the effectiveness of the proposed design. The system exhibited a fast transient response of 0.05 s during grid-support operation and maintained stable voltage and frequency in off-grid mode despite PV and load fluctuations. Its protection scheme disconnected overloads within 0.01 s, while harmonic distortions in both cases remained modest and complied with EN50610 standards. Full article

The Organic Rankine Cycle (ORC) offers an efficient approach for harnessing low-grade thermal energy. However, ORC systems often struggle to achieve maximum output power when subject to fluctuations in sink and heat source temperatures. To address this challenge, this paper proposes a Maximum Power Point Tracking (MPPT) strategy based on the optimal evaporation pressure and superheat degree, enabling ORC systems to achieve maximum power output even under varying thermal conditions. First, a dynamic model of the ORC system is established, and the variations in key parameters under different expander and working fluid pump speeds are analyzed. Based on this analysis, the MPPT strategy is developed and its performance is verified through simulations under fluctuating sink and heat source temperatures. The results demonstrated that the ORC system must simultaneously adjust both the expander speed and the working fluid pump speed to maximize power output. Moreover, there exist optimal values of evaporation pressure and superheat degree that yield maximum system performance. Compared with the optimal evaporation pressure strategy, the proposed MPPT approach improves power generation by 14.15%. Full article

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability. Full article

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article

Transformerless inverters have received significant attention in solar photovoltaic (PV) applications. The absence of low-frequency transformers contributes to improved efficiency and reduced size compared to other topologies; however, there are concerns about leakage currents. The common ground (CG) connection in PV inverters is an attractive solution to this issue, as it generates a constant common-mode voltage and theoretically eliminates the leakage current. In this context, multilevel CG inverters can eliminate the leakage current while achieving high-quality output voltages. Nonetheless, achieving simultaneous control of the grid current and inner capacitor voltages can be challenging. Furthermore, controlling the capacitor voltages in multilevel inverters requires feedback from measurement sensors, which can increase the cost and may affect the overall reliability. To address these issues, this paper proposes a model predictive controller (MPC) for a CG multilevel inverter with a reduced number of sensors. While conventional MPC uses a classical multi-objective technique with a single cost function, the proposed method avoids the use of weighting factors in the cost function. Additionally, a sliding-mode observer is developed to estimate the capacitor voltages, and an incremental conductance-based maximum power point tracking (MPPT) algorithm is used to generate the current reference. Simulation and experimental results confirm the effectiveness of the proposed observer and MPC strategy. 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

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail to maintain optimal power output. Dynamic load changes and complex operating conditions exacerbate this issue. As a result, system response is slowed, and tracking accuracy is reduced. To address these problems, an online identification method based on recursive least squares (RLS) is employed. A cubic power–current model is identified in real time. Polynomial fitting and the golden section search are then applied to estimate the current at the maximum power point. Following model-based estimation, adaptive particle swarm optimization (APSO) is utilized to tune the PID controller parameters. Precise regulation is thus achieved. The use of RLS enables real-time model identification. The golden section search improves the efficiency of current estimation. APSO enhances global optimization, while PID provides fast dynamic response. By integrating these methods, both tracking accuracy and system responsiveness are significantly improved in fuel cell MPPT applications. Simulation results demonstrate that the proposed strategy enhances maximum power output by up to 12.40% compared to conventional P&O, fuzzy logic control, GWO-PID, and PSO-PID methods, as well as maintaining a consistent improvement of 1.50% to 1.90% even when compared to other optimization algorithms. Full article

The pressing global transition to sustainable energy has intensified interest in overcoming the efficiency bottlenecks of conventional solar technologies. Hybrid photovoltaic–thermoelectric generator (PV–TEG) systems have recently emerged as a compelling solution, synergistically harvesting both electrical and thermal energy from solar radiation. By converting both sunlight and otherwise wasted heat, these integrated systems can substantially enhance total energy yield and overall conversion efficiency—mitigating the performance limitations of standalone PV panels. This review delivers a comprehensive, systematic assessment of maximum-power-point tracking (MPPT) methodologies specifically tailored for hybrid PV–TEG architectures. MPPT techniques are meticulously categorized and critically analyzed within the following six distinct groups: conventional algorithms, metaheuristic approaches, artificial intelligence (AI)-driven methods, mathematical models, hybrid strategies, and novel emerging solutions. For each category, we examine operational principles, implementation complexity, and adaptability to real-world phenomena such as partial shading and non-uniform temperature distribution. Through thorough comparative evaluation, the review uncovers existing research gaps, highlights ongoing challenges, and identifies promising directions for technological advancement. This work equips researchers and practitioners with an integrated knowledge base, fostering informed development and deployment of next-generation MPPT solutions for high-performance hybrid solar–thermal energy systems. Full article

Power loss can influence the accuracy of maximum power point tracking (MPPT) control and the efficiency of a brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). Because power loss is related to both the active power reference and reactive power reference of BDFG, this article proposes active and reactive power optimal control of BDFGWT by considering power loss. Firstly, the mathematical model of BDFGWT, including the wind turbine, BDFG, and back-to-back converter, is established. Then, an active and reactive power optimal control strategy is proposed. In proposed control, the accurate active power reference of power winding (PW) is calculated by considering the active power loss of BDFG; in this way, proposed MPPT control can capture more wind power compared to traditional MPPT control, ignoring the power losses, thus improving the efficiency of BDFGWT. Furthermore, on the basis of the model of BDFG, the relations between reactive power and total active loss are analyzed, and the optimal reactive power control reference to minimize the active power loss is determined. Finally, in order to verify the validity of the proposed control, 2 MW BDFGWT has been constructed, and the proposed method was studied to make a comparison. The results verify that proposed control can maximize the utilization of wind energy, minimize the power loss of the BDFGWT system, and output maximal active power to the power grid. Full article

In partial shading conditions (PSCs), the power–voltage characteristics of photovoltaic systems exhibit multiple peaks, causing traditional maximum power point tracking (MPPT) algorithms to easily become trapped in local optima and fail to achieve global maximum power point tracking, thereby reducing energy conversion efficiency. Effectively and rapidly locating the global maximum power under complex environmental conditions has become crucial for enhancing MPPT performance in photovoltaic systems. This paper therefore proposes an improved elk herd optimization (IEHO) algorithm to achieve the rapid tracking of the global maximum power point under various weather conditions. The algorithm proposes a position update mechanism guided by the predation risk probability to direct elk herd migration and introduces the triangle walk strategy, thereby enhancing the algorithm’s capability to avoid local optima. Furthermore, IEHO employs a memory-guided redirection strategy to skip redundant calculations of historical duty cycles, significantly improving the convergence speed of MPPT. To validate the algorithm’s performance advantages, the proposed IEHO method is compared with other recognized meta-heuristic algorithms under various weather conditions. The experimental results demonstrate that, across all tested conditions, the proposed IEHO method achieves an average tracking efficiency of 99.99% and an average tracking time of 0.3886 s, outperforming other comparative algorithms. Full article

This work investigates the application of artificial intelligence techniques for optimizing photovoltaic systems using maximum power point tracking (MPPT) algorithms. Simulation models were developed in MATLAB/Simulink (Version 2024), incorporating conventional and intelligent control strategies such as fuzzy logic, genetic algorithms, neural networks, and Deep Reinforcement Learning. A DC/DC buck converter was designed and tested under various irradiance and temperature profiles, including scenarios with partial shading conditions. The performance of the implemented MPPT algorithms was evaluated using such metrics as Mean Absolute Error (MAE), Integral Absolute Error (IAE), mean squared error (MSE), Integral Squared Error (ISE), efficiency, and convergence time. The results highlight that AI-based methods, particularly neural networks and Deep Q-Network agents, outperform traditional approaches, especially in non-uniform operating conditions. These findings demonstrate the potential of intelligent controllers to enhance the energy harvesting capability of photovoltaic systems. Full article

This paper proposes a novel control strategy for a dual-stage grid-connected solar photovoltaic (PV) system designed to ensure reliable and efficient operation under unstable grid conditions. The strategy incorporates a Phase-Locked Loop (PLL)-based positive sequence estimator for accurate detection of grid voltage disturbances, including sags, swells, and fluctuations in solar irradiance. A dynamic DC-link voltage regulation mechanism is employed to minimize converter power losses and enhance the performance of the Voltage Source Converter (VSC) under weak grid scenarios. The control scheme maintains continuous maximum power point tracking (MPPT) and unity power factor (UPF) operation, thereby improving overall grid power quality. The proposed method is validated through comprehensive simulations and real-time hardware implementation using the OPAL-RT OP4510 platform. The results demonstrate compliance with IEEE Standard 519, confirming the effectiveness and robustness of the proposed strategy. Full article

This paper addresses the challenge of maximizing power extraction in offshore wind energy systems through the development of an enhanced maximum power point tracking (MPPT) control strategy. Offshore wind energy is inherently intermittent, leading to discrepancies between power generation and electricity demand. To address this issue, we propose three advanced control algorithms to perform a comparative analysis: sliding mode control (SMC), the Integral Backstepping-Based Real-Twisting Algorithm (IBRTA), and Feed-Back Linearization (FBL). These algorithms are designed to handle the nonlinear dynamics and aerodynamic uncertainties associated with offshore wind turbines. Given the practical limitations in acquiring accurate nonlinear terms and aerodynamic forces, our approach focuses on ensuring the adaptability and robustness of the control algorithms under varying operational conditions. The proposed strategies are rigorously evaluated through MATLAB/Simulink 2024 A simulations across multiple wind speed scenarios. Our comparative analysis demonstrates the superior performance of the proposed methods in optimizing power extraction under diverse conditions, contributing to the advancement of MPPT techniques for offshore wind energy systems. 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

This paper presents a comprehensive summary of recent advances in circuit topologies for piezoelectric energy harvesting, leading to self-powered systems (SPSs), covering the full-bridge rectifier (FBR) and half-bridge rectifier (HBR), AC-DC converters, and maximum power point tracking (MPPT) techniques. These approaches are analyzed with respect to their advantages, limitations, and overall impact on energy harvesting efficiency. Th work explores alternative methods that leverage phase shifting between voltage and current waveform components to enhance conversion performance. Additionally, it provides detailed insights into advanced design strategies, including adaptive power management algorithms, low-power control techniques, and complex impedance matching. The paper also addresses the fundamental principles and challenges of converting mechanical vibrations into electrical energy. Experimental results and performance metrics are reviewed, particularly in relation to hybrid approaches, load impedance, vibration frequency, and power conditioning requirements in energy harvesting systems. This review aims to provide researchers and engineers with a critical understanding of the current state of the art, key challenges, and emerging opportunities in piezoelectric energy harvesting. By examining recent developments, it offers valuable insights into optimizing interface circuit design for the development of efficient and self-sustaining piezoelectric energy harvesting systems. Full article