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Search Results (308)

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Keywords = MPPT techniques

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25 pages, 11967 KiB  
Article
Quadrature-Phase-Locked-Loop-Based Back-Electromotive Force Observer for Sensorless Brushless DC Motor Drive Control in Solar-Powered Electric Vehicles
by Biswajit Saha, Aryadip Sen, Bhim Singh, Kumar Mahtani and José A. Sánchez-Fernández
Appl. Sci. 2025, 15(2), 574; https://doi.org/10.3390/app15020574 - 9 Jan 2025
Viewed by 262
Abstract
This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations [...] Read more.
This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations of existing control methods at low speeds and under dynamic conditions. The study replaces the conventional arc-tangent technique with a QPLL-based approach, eliminating low-pass filters to enhance system adaptability and reduce delays. The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. Furthermore, the performance of a modified single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) in solar-powered LEVs is verified, minimizing current ripple and ensuring smooth motor operation. The system also incorporates a regenerative braking mechanism, extending the vehicle’s range by efficiently recovering kinetic energy through the battery with 30.60% efficiency. The improved performance of the proposed method and system over conventional approaches contributes to the advancement of efficient and sustainable solar-powered BLDC motor-based EV technologies. Full article
(This article belongs to the Special Issue Design and Synthesis of Electric Energy Conversion Systems)
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19 pages, 4679 KiB  
Article
Development and Implementation of the MPPT Based on Incremental Conductance for Voltage and Frequency Control in Single-Stage DC-AC Converters
by Javier Alonso Ramírez Torres, Orlando Lastres Danguillecourt, Roberto Adrián González Domínguez, Guillermo Rogelio Ibáñez Duharte, Laura Elena Verea Valladares, Joel Pantoja Enríquez, Jesús Antonio Enríquez Santiago, Andrés López López and Antonio Verde Añorve
Energies 2025, 18(1), 184; https://doi.org/10.3390/en18010184 - 4 Jan 2025
Viewed by 468
Abstract
This paper presents the design, simulation, and experimental evaluation of a low-cost, fixed-step MPPT algorithm based on the incremental conductance technique for operation in a low-power photovoltaic (PV) system with a full-bridge DC-AC converter. The performance of the MPPT algorithm was improved by [...] Read more.
This paper presents the design, simulation, and experimental evaluation of a low-cost, fixed-step MPPT algorithm based on the incremental conductance technique for operation in a low-power photovoltaic (PV) system with a full-bridge DC-AC converter. The performance of the MPPT algorithm was improved by selecting an appropriate fixed perturbation step size and frequency, ensuring efficient power tracking. The implementation was further optimized by restructuring the conventional algorithm and adapting the DC-AC converter control parameters, which enhanced overall performance and optimized coupling for AC loads. The simulation was performed in Simulink/Matlab with a 560 Wp PV system and a resistive load, under variable irradiation conditions. The perturbation step size was set to 1%, and the perturbation frequency ranged between 2 Hz and 15 Hz, with the converter output at 60 Hz. Experimentally, it was validated at an irradiance of 1000 W/m2 and an ambient temperature of 45 °C. The algorithm achieved simulation efficiencies of up to 98.93% and an average experimental efficiency of 96.76%. The response time improved by 86% with a perturbation frequency of 15 Hz. This developed MPPT algorithm demonstrates its reliability, accuracy, and feasibility for implementation. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 3rd Edition)
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26 pages, 16984 KiB  
Article
An Enhanced Solar Battery Charger Using a DC-DC Single-Ended Primary-Inductor Converter and Fuzzy Logic-Based Control for Off-Grid Photovoltaic Applications
by Julio López Seguel, Samuel Zenteno, Crystopher Arancibia, José Rodríguez, Mokthar Aly, Seleme I. Seleme and Lenin M. F. Morais
Processes 2025, 13(1), 99; https://doi.org/10.3390/pr13010099 - 3 Jan 2025
Viewed by 507
Abstract
Battery charging systems are crucial for energy storage in off-grid photovoltaic (PV) installations. Since the power generated by a PV panel is conditioned by climatic conditions and load characteristics, a maximum power point tracking (MPPT) technique is required to maximize PV power and [...] Read more.
Battery charging systems are crucial for energy storage in off-grid photovoltaic (PV) installations. Since the power generated by a PV panel is conditioned by climatic conditions and load characteristics, a maximum power point tracking (MPPT) technique is required to maximize PV power and accelerate battery charging. On the other hand, a battery must be carefully charged, ensuring that its charging current and voltage limits are not exceeded, thereby preventing premature degradation. However, the voltage generated by the PV panel during MPPT operation fluctuates, which can harm the battery, particularly during periods of intense radiation when overvoltages are likely to occur. To address these issues, the design and construction of an enhanced solar battery charger utilizing a single-ended primary-inductor converter (SEPIC) and soft computing (SC)-based control is presented. A control strategy is employed that integrates voltage stabilization and MPPT functions through two dedicated fuzzy logic controllers (FLCs), which manage battery charging using a three-mode scheme: MPPT, Absorption, and Float. This approach optimizes available PV power while guaranteeing fast and safe battery charging. The developed charger leverages the SEPIC’s notable features for PV applications, including a wide input voltage range, minimal input current ripple, and an easy-to-drive switch. Moreover, unlike most PV charger control strategies in the literature that combine improved traditional MPPT methods with classical proportional integral (PI)-based control loops, the proposed control adopts a fully SC-based strategy, effectively addressing common drawbacks of conventional methods, such as slowness and inaccuracy during sudden atmospheric fluctuations. Simulations in MATLAB/Simulink compared the FLCs’ performance with conventional methods (P&O, IncCond, and PID). Additionally, a low-power hardware prototype using an Arduino Due microcontroller was built to evaluate the battery charger’s behavior under real weather conditions. The simulated and experimental results both demonstrate the robustness and effectiveness of the solar charger. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Systems (2nd Edition))
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25 pages, 2604 KiB  
Article
Enhancing Efficiency in Hybrid Solar–Wind–Battery Systems Using an Adaptive MPPT Controller Based on Shadow Motion Prediction
by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, Nasrin Salehi, Mahyar Moradi, José J. M. Machado and João Manuel R. S. Tavares
Appl. Sci. 2024, 14(24), 11710; https://doi.org/10.3390/app142411710 - 16 Dec 2024
Viewed by 798
Abstract
Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to [...] Read more.
Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m2 solar farm under shaded conditions. Full article
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22 pages, 7077 KiB  
Article
Maximum Power Point Tracking Based on Finite Voltage-Set MPC for Grid-Connected Photovoltaic Systems Under Environmental Variations
by Mohammed A. Hassan, Mahmoud M. Adel, Amr A. Saleh, Magdy B. Eteiba and Ahmed Farhan
Sustainability 2024, 16(23), 10317; https://doi.org/10.3390/su162310317 - 25 Nov 2024
Viewed by 561
Abstract
This paper proposes a model predictive control (MPC)-based approach for optimizing the performance of a photovoltaic (PV) system. The proposed method employs finite voltage-set maximum power point tracking (FVS-MPPT), ensuring precise duty cycle adjustment for a boost converter in the PV system considering [...] Read more.
This paper proposes a model predictive control (MPC)-based approach for optimizing the performance of a photovoltaic (PV) system. The proposed method employs finite voltage-set maximum power point tracking (FVS-MPPT), ensuring precise duty cycle adjustment for a boost converter in the PV system considering the environmental changes in irradiation and temperature. Additionally, MPC is implemented for the grid-side converter to determine the optimal switching vector, ensuring precise control of active power via reference d-axis current and the elimination of reactive power by setting the reference q-axis current to zero. This approach optimizes the converter’s performance, maintaining a stable DC-link voltage while ensuring efficient grid integration. To ensure proper synchronization with the grid, a phase-locked loop (PLL) is utilized to provide the necessary grid voltage angle for dq frame transformation. Simulation results highlight the efficiency of the proposed MPC strategy, with the PV-side converter showing a robust response by dynamically adjusting the duty cycle to maintain optimal performance under varying irradiation and temperature conditions. Furthermore, the grid-side converter ensures precise control of active power and eliminates reactive power, enhancing the overall system’s stability and efficiency during grid interactions. A functional comparison of simulation results between the conventional P&O algorithm and the FVS-MPPT approach is presented, demonstrating the enhanced performance of the proposed technique over the conventional method including the total harmonic distortion for both techniques. Full article
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28 pages, 5707 KiB  
Review
Review on Maximum Power Point Tracking Control Strategy Algorithms for Offshore Floating Photovoltaic Systems
by Lei Huang, Baoyi Pan, Shaoyong Wang, Yingrui Dong and Zihao Mou
J. Mar. Sci. Eng. 2024, 12(12), 2121; https://doi.org/10.3390/jmse12122121 - 21 Nov 2024
Viewed by 576
Abstract
Floating photovoltaic systems are rapidly gaining popularity due to their advantages in conserving land resources and their high energy conversion efficiency, making them a promising option for photovoltaic power generation. However, these systems face challenges in offshore environments characterized by high salinity, humidity, [...] Read more.
Floating photovoltaic systems are rapidly gaining popularity due to their advantages in conserving land resources and their high energy conversion efficiency, making them a promising option for photovoltaic power generation. However, these systems face challenges in offshore environments characterized by high salinity, humidity, and variable irradiation, which necessitate effective maximum power point tracking (MPPT) technologies to optimize performance. Currently, there is limited research in this area, and few reviews analyze it comprehensively. This paper provides a thorough review of MPPT techniques applicable to floating photovoltaic systems, evaluating the suitability of various methods under marine conditions. Traditional algorithms require modifications to address the drift phenomena under uniform irradiation, while different GMPPT techniques exhibit distinct strengths and limitations in partial shading conditions (PSCs). Hardware reconfiguration technologies are not suitable for offshore use, and while sampled data-based techniques are simple, they carry the risk of erroneous judgments. Intelligent technologies face implementation challenges. Hybrid algorithms, which can combine the advantages of multiple approaches, emerge as a more viable solution. This review aims to serve as a valuable reference for engineers researching MPPT technologies for floating photovoltaic systems. Full article
(This article belongs to the Special Issue Offshore Renewable Energy, Second Edition)
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18 pages, 9214 KiB  
Article
Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data
by Bappa Roy, Shuma Adhikari, Subir Datta, Kharibam Jilenkumari Devi, Aribam Deleena Devi and Taha Selim Ustun
Electricity 2024, 5(4), 843-860; https://doi.org/10.3390/electricity5040042 - 4 Nov 2024
Viewed by 1216
Abstract
Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar [...] Read more.
Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the ESCI Coverage)
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25 pages, 1715 KiB  
Article
Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions
by Okba Fergani, Yassine Himeur, Raihane Mechgoug, Shadi Atalla, Wathiq Mansoor and Nacira Tkouti
Information 2024, 15(11), 692; https://doi.org/10.3390/info15110692 - 3 Nov 2024
Viewed by 704
Abstract
The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions [...] Read more.
The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
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20 pages, 8941 KiB  
Article
Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions
by Zhuoxuan Li, Changxin Fu, Lixin Zhang and Jiawei Zhao
Electronics 2024, 13(21), 4148; https://doi.org/10.3390/electronics13214148 - 22 Oct 2024
Viewed by 786
Abstract
The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in [...] Read more.
The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times. Full article
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25 pages, 12723 KiB  
Article
A Dynamic Simulation of a Piezoelectric Energy-Harvesting System Integrated with a Closed-Loop Voltage Source Converter for Sustainable Power Generation
by Ahmed K. Ali, Ali Abdulwahhab Abdulrazzaq and Ali H. Mohsin
Processes 2024, 12(10), 2198; https://doi.org/10.3390/pr12102198 - 10 Oct 2024
Viewed by 1247
Abstract
Numerous recent studies address the concept of energy harvesting from natural wind excitation vibration to piezoelectric surfaces, aerodynamic losses, and electromagnetic dampers. All these techniques require a connection to an energy-management circuit. However, the simulation model for energy conversion and management dedicated to [...] Read more.
Numerous recent studies address the concept of energy harvesting from natural wind excitation vibration to piezoelectric surfaces, aerodynamic losses, and electromagnetic dampers. All these techniques require a connection to an energy-management circuit. However, the simulation model for energy conversion and management dedicated to this task has not yet been described. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy-harvester system (PEHS) technology. A controlled pulse width modulation (PWM) rectifier, a closed-loop buck-boost converter, and a piezoelectric transducer comprise a dynamic mathematical model of a PEHS. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrated that the proposed dynamic PEHS model effectively fed low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives. Full article
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23 pages, 6135 KiB  
Article
Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
by Anshuman Satpathy, Rahimi Bin Baharom, Naeem M. S. Hannon, Niranjan Nayak and Snehamoy Dhar
Energies 2024, 17(20), 5024; https://doi.org/10.3390/en17205024 - 10 Oct 2024
Viewed by 911
Abstract
This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as [...] Read more.
This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. Full article
(This article belongs to the Topic Advanced Energy Harvesting Technology)
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23 pages, 2337 KiB  
Article
Comparative Evaluation of Traditional and Advanced Algorithms for Photovoltaic Systems in Partial Shading Conditions
by Robert Sørensen and Lucian Mihet-Popa
Solar 2024, 4(4), 572-594; https://doi.org/10.3390/solar4040027 - 8 Oct 2024
Viewed by 1140
Abstract
The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic [...] Read more.
The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in the MATLAB/Simulink software package evaluated these algorithms’ performances under various shading scenarios. The results indicate that, while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. This study concludes that integrating Artificial Intelligence (AI) and Machine Learning (ML) into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for a broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and supporting green energy transition. Full article
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14 pages, 3165 KiB  
Article
Optimized Nonlinear PID Control for Maximum Power Point Tracking in PV Systems Using Particle Swarm Optimization
by Maeva Cybelle Zoleko Zambou, Alain Soup Tewa Kammogne, Martin Siewe Siewe, Ahmad Taher Azar, Saim Ahmed and Ibrahim A. Hameed
Math. Comput. Appl. 2024, 29(5), 88; https://doi.org/10.3390/mca29050088 - 2 Oct 2024
Cited by 1 | Viewed by 1139
Abstract
This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by [...] Read more.
This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by a metaheuristic algorithm called Particle Swarm Optimization (PSO). The proposed methods appear to present adequate solutions to overcome the drawbacks of existing methods despite various weather conditions considered in the analysis, providing a robust solution for dynamic environmental conditions. The results showed better performance and accuracy compared to those encountered in the literature. We also recall that this technique provides a systematic design procedure in the search for the MPPT in photovoltaic (PV) systems that has not yet been documented in the literature to the best of our knowledge. Full article
(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)
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13 pages, 1659 KiB  
Article
Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies
by Arun Selvaraj and Ganesh Mayilsamy
Wind 2024, 4(4), 275-287; https://doi.org/10.3390/wind4040014 - 2 Oct 2024
Viewed by 987
Abstract
This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to [...] Read more.
This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system. Full article
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36 pages, 28072 KiB  
Article
Four-Wire Three-Level NPC Shunt Active Power Filter Using Model Predictive Control Based on the Grid-Tied PV System for Power Quality Enhancement
by Zoubida Amrani, Abdelkader Beladel, Abdellah Kouzou, Jose Rodriguez and Mohamed Abdelrahem
Energies 2024, 17(15), 3822; https://doi.org/10.3390/en17153822 - 2 Aug 2024
Viewed by 1110
Abstract
The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the [...] Read more.
The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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