MPPT Research Articles

Design of a two-stage photovoltaic grid-connected system with dual closed-loop control

Abstract This paper designs a two-stage photovoltaic grid-connected system with dual closed-loop control, cascading the topological structures of photovoltaic cells, boost chopper circuits, and inverter circuits. The front-stage boost chopper circuit control utilizes the incremental conductance method to achieve maximum power point tracking control. The rear-stage inverter circuit employs a dual closed-loop control system with an outer voltage loop and an inner current loop, incorporating a proportional-integral controller, to ensure stable DC-side voltage and unity power factor for grid connection. A simulation model of the two-stage photovoltaic grid-connected system designed in this paper was constructed and analyzed through simulation runs. The firmness of the DC-side voltage and the achievement of unity power factor during grid connection confirmed the operability and resultiveness of the cascaded topology and the control method.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems

This paper proposes a new maximum power point tracking (MPPT) method based on machine learning with improved power production efficiency for application to photovoltaic (PV) systems. Power loss occurs in the incremental conductance (InC) method, depending on the size of the voltage step used to track the maximum power point. Additionally, the size of the voltage step must be specified by the initial user; however, an appropriate size cannot be determined in a rapidly changing environment. To solve this problem, this study presents a gradient descent InC (GD InC) method that optimizes the size of the voltage step by applying an optimization method based on machine learning. The effectiveness of the GD InC method was verified and the optimized size of the voltage step was confirmed to produce the largest amount of power. When the size of the voltage step was optimized, a maximum difference of 4.53% was observed compared with the case when the smallest amount of power was produced. The effectiveness of the GD InC method, which improved the efficiency of power production by optimizing the size of the voltage step, was verified. Power can be produced efficiently by applying the GD InC method to PV systems.

Efficient and cost-effective maximum power point tracking technique for solar photovoltaic systems with Li-ion battery charging

This paper presents an effective approach to achieve maximum power point tracking (MPPT) in photovoltaic (PV) systems for battery charging using a single-sensor incremental conductance (InC) method. The objective is to optimize the MPPT process while minimizing the number of sensors required. The suggested technique leverages the relationship between the PV module's output voltage and the duty cycle to automatically adjust and reach the MPP, resulting in optimal power generation. By eliminating the PV current sensor from the control circuit, the developed method reduces both the cost and size of the MPPT circuit. Compared to the conventional InC method, the developed approach demonstrates improved response speed and accuracy in steady-state operation, along with the ability to damp oscillations near the MPP. Extensive simulations using MATLAB/Simulink validate the performance of the developed technique across various environmental conditions. The results highlight the recommended method's realistic and effective MPP tracking capabilities, achieving higher efficiency (99.12 %) compared to the classical method (97.8 %) under high irradiance levels.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

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.

Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.

A Comparative Investigation of Hybrid MPPT Methods for Enhancing Solar Power Generation in Renewable Energy Systems

Photovoltaic (PV) systems are among the types of renewable energy that are frequently employed. Since the characteristics of the solar cell depend on the amount of insolation and temperature, it is necessary to use MPPT “Maximum Power Point Tracking” to move the operating voltage close to the maximum power point under changing weather conditions. This article aims to design a photovoltaic energy system based on boost converter control to obtain maximum power using a hybrid algorithm based on artificial neurons (ANN). Additionally included is a proportional-integral (PI) controller, which improves the performance of the ANN-MPPT controller; this method is quick and precise for tracking the maximum power point (MPP) in the face of variations in temperature and solar radiation. The efficiency of the tracking algorithm was calculated and compared to one of the traditional methods, the incremental conductance (INC) method, in addition to comparing it to other hybrid methods and by simulating the system using MATLAB/Simulation and analyzing the results. This study unequivocally proves the superiority of the hybrid ANN+PI strategy; the efficiency reached 99.91%. This approach excels at tracking maximum power accuracy by leveraging the adaptive learning capabilities of neural networks, ensuring maximum power even in the face of changing environmental conditions.

Integration of MPPT algorithms with spacecraft applications: Review, classification and future development outlook

The electric power system is one of the key subsystems of a spacecraft and is responsible for the acquisition, conversion, storage and control of energy during the spacecraft mission. Therefore, ensuring the adequacy and stability of energy sources has become an important part of the spacecraft design process. Currently, most spacecraft acquire energy through the solar cell arrays they carry, but the actual output power of the photovoltaic system is difficult to reach the peak power due to the influence of the on-orbit space environment such as alternating temperature changes and radiation. In order to improve the output efficiency of PV systems, researchers have proposed the maximum power point tracking method based on the operating characteristics of the PV cells, and the classical methods include the incremental conductance method, hill-climbing method, constant voltage method, and perturbation observation method. On its basis, in order to further improve the control accuracy and adaptability of MPPT under complex environment, related scholars have proposed several new MPPT algorithms, such as fuzzy algorithm and neural network algorithm based on the principle of intelligence, particle swarm optimization algorithm based on the principle of optimization, gray wolf algorithm and hybrid optimization algorithm based on multiple algorithms.This paper provides a comprehensive review of the topology, hardware architecture, mathematical models, and multiple DC-DC circuits of maximum power tracking PV systems; and reviews and summarizes the classical, intelligent, optimization, and hybrid types of maximum power point tracking methods, describing the principles, advantages, disadvantages, and applications of the different methods, respectively. Finally, an outlook on the future development and novel applications of maximum power point tracking methods for photovoltaic systems in aerospace is presented.

Operational Verification of DC-DC Converters Used in Ecologically Sound Non Polluting Renewable Systems

This research delves into the intricate control mechanisms governing renewable energy sources through the application of three distinct DC-DC converter technologies: the buck, boost, and buck-boost converters. Renewable energy sources like wind and solar can be pivotal in the future and become the primary source of energy for global energy requirements. Renewable sources are pollution-free and have minimal impact on the environment. This research illuminates the efficacy of these converters in orchestrating the optimal utilization of renewable energy sources, offering valuable contributions to the advancement of sustainable power systems. Open and closed loop control of a buck converter is studied along with its operation for the voltage regulation of an emulated PV panel. For the boost converter, a comprehensive exploration ensues through two closed-loop control methods: Perturb and Observe (P&O) algorithm and the incremental conductance method. Simulations are meticulously executed in MATLAB Simulink, interfacing with PV arrays and directing power toward battery charging. Meanwhile, the buck-boost converter undergoes simulation and evaluation, fed by a DC source and delivering power to a resistive load. Experimental validation is conducted for the buck converter, providing tangible insights into real-world performance. Conventional sources, primarily coal, which is the most used source of energy in Asian countries, including India, have adverse effects on the environment. Coal contributes to a huge proportion of pollutants in the environment and also the release of large quantities of greenhouse gases. Renewable energy resources can play a huge role in addressing the energy demands of Asian countries. They hold the ability to alleviate the environmental burden caused by conventional energy sources. Renewable energy sources also mitigate pollution and eliminate various hazardous effects of conventional energy sources. This study emphasises the adoption of such sources and underscores the pivotal role of DC-DC converter technologies in this adoption.

A novel adaptive and modified controller for tracking global peak under partial shading conditions

The present state of Maximum Power Point Tracking (MPPT) techniques face challenges in accurately tracking the Global Maximum Power Point (GMPP) under non-uniform conditions. Swift transitions between operational points, significant transient fluctuations, and imprecise tracking contribute to notable power losses. To address these issues, a novel modified Perturb and Observe (P&O) algorithm is proposed. It mitigates persistent oscillations and enhances tracking accuracy in photovoltaic (PV) systems. Compared to traditional P&O and incremental conductance methods, the proposed algorithm achieves approximately 9.52 % higher average MPPT efficiency. Even under challenging conditions like partial shading, it maintains superior performance with faster tracking and less complexity. The algorithm ensures proper power sharing, achieving an average maximum stabilized power efficiency of 98.48 % with a tracking time below 0.06 s. Notably, it exhibits a 16.5 % increase in power increment across various shading scenarios. Additionally, the proposed method complies with IEC 60904-3 standards, minimizing voltage and current deviations during irradiance dynamics. MATLAB/Simulink and hardware experiments confirm the efficacy of the algorithm, demonstrating improved stability and reduced settling time. The proposed method outperforms existing algorithms, exhibiting the lowest mismatch power loss and achieving a remarkable 15.08 % enhanced power increment. This research contributes to advancing MPPT techniques, offering improved performance and stability in PV systems.

Evaluation of Optimum MPPT Controllers for PV Cells using Matlab Simulink Platform

Abstract: Nowadays, the Photovoltaic cell is one of the most essential parts in the electrical field to convert photo light to voltage and current, at the desired output voltage and frequency by using various control techniques The Solar panel can produce maximum power at a particular operating point called Maximum Power Point (MPP).To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus to extract maximum available power from a PV module, MPPT algorithms are implemented. The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement MPPT controllers to obtain the maximum power of a PV system regardless of variations in climatic conditions. This project reviews the most used MPPT algorithms, which are Perturb and observe (P&O), Incremental conductance method (ICM), and Fuzzy logic control (FLC).

Implementing Different MPPT Algorithm For Solar Charge Controller

In this paper, MPPT methods such as Perturb and Observe (P and O), Incremental Conductance and Fractional open Circuit Voltage method has been studied. Also, two converter topology, Buck and Synchronous buck converter has been Studied. Hardware implementation of the solar charge controller is done based on the software simulation data and results. Hardware is developed for 1kW system and 48V, 100Ah battery is going to be charged with it. Two MPPT method with synchronous buck converter has been implemented in hardware. This designed hardware has been tested with different irradiance condition. This proposed system can be used in golf cart to make it a stand-alone system with taking care of all protection measures.

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

A PV MPPT control method based on async-PSO and INC algorithm under shading condition

For the time being, solar energy has received considerable attention and development on account of its distinct advantages, such as rich reserves and no geographical restrictions. Nevertheless, in practical applications, the photovoltaic module is easily affected by external environments, which gives rise to a decrease in photovoltaic power. The maximum power point tracking (MPPT) technology for PV power system is an effective method to elevate the efficacy of photovoltaic electricity conversion. The frequently used control methods include the perturb and observe (P&O) algorithm and the incremental conductance (INC) method, and so forth; these methods vary tremendously in terms of the required parameters, algorithm complexity, tracking speed, tracking accuracy, hardware requirements etc. This work puts forth a MPPT control method on the basis of Async-PSO and INC algorithm to achieve a better performance in the MPPT. To reflect the change of light amplitude and temperature in a day, the temperature varies from 25℃ to 60℃ and irradiance from 450W/m2 to 900W/m2 . An extensively used mono-crystalline silicon PV module with 240W was considered as the research object to compare the capability of the recommended MPPT control method. MATLAB/Simulink software was adopted to model and simulate the algorithm. Aside from that, comprehensive comparisons were made with other MPPT methods to test and verify the recommended algorithm has significantly improved the tracking speed and accuracy at the maximum power point with smaller oscillations under various conditions.

Simulation based study of Maximum Power Point Tracking and Frequency Regulation for Stand-alone Solar Photovoltaic Systems

The main objective of this paper is to develop a simulation model of stand-alone solar photovoltaic (PV) system based on mathematical models. A maximum power point tracking (MPPT) algorithm based on incremental conductance method is applied to extract the maximum power from solar PV energy conversion system. A DC/DC boost converter allows the MPPT to control the intermediate voltage. To regulate to voltage at load side a voltage source converter (VSC) based controller is applied to generate the pulse signal for three-phase inverter. The frequency regulation controller is developed based on conventional phase locked loop (PLL) system along with resistive dumping loads. A simulation model based on mathematical model Matlab®/Simulink®/SimPower® is constructed interface. Results using from simulation are stated to demonstrate the behaviour of stand alone solar PV energy conversion system.

Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems

Background: One of the key challenges in maximizing the performance of PV systems is the efficient tracking of the maximum power point (MPP) under varying operational conditions, including changes in solar irradiance and temperature. Accurate MPP tracking is essential for achieving optimal energy conversion efficiency and maximizing the electricity generation potential of the PV array, even during partial shading conditions. Traditional maximum power point tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to efficiently handle partial shading conditions. As a result, there is a need for more sophisticated and robust optimization techniques that can effectively address this challenge. This study presents a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shadinginduced MPP tracking problem. Objective: This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in photovoltaic systems under partial shading conditions, aiming to enhance energy conversion efficiency and overall system performance. Methods: The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter, enabling efficient power extraction from the PV array under varying shading conditions. The performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and compared against conventional MPPT methods like INC and DGO techniques. Results: The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in PV systems under partial shading led to significantly reduced optimization time, faster settling times, and minimized output ripples. With the proposed GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples are minimized to 0.092%. Conclusion: the Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a robust and efficient maximum power point tracking method in photovoltaic systems, particularly under partial shading conditions. The improved optimization speed, reduced settling times, and minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency and reliability of PV systems, paving the way for its practical implementation in real-world renewable energy applications.

Improving the incremental conductance control method of a solar energy conversion system

In the context of renewable energy, this study treats the case of the conversion of solar energy, which is one part of non pollutant energy, to electrical one. Maximizing methods of the generated power is discussed in this paper. Thus, the case of solar panel is studied and a compariso between different strategies of the incremental conductance method in order to maximize the output power is given, analyzed, compared and discussed.

A Novel Dynamic and Self-Adaptive InCre Technique Based on PI Control and EO Optimization for Hybrid PV-TEG Conversion Systems

This work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment.

A stochastic variance reduction gradient-based GSO-ANFIS optimized method for maximum power extraction of proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cells (PEMFCs) play a pivotal role in the clean energy landscape, yet their efficiency is contingent upon effective power optimization. This paper presents Maximum PowerPoint Tracking (MPPT) control schemes for PEMFCs, focusing on a ground-breaking methodology. Traditional MPPT controllers are instrumental in maintaining optimal performance; however, they often struggle with dynamic operating conditions. In response to this challenge, this research work presents a pioneering MPPT control scheme employing a stochastic variance reduction gradient system. The novelty of this approach lies in its fusion with the Glow Swarm Optimization (GSO) and the Adaptive Neuro Fuzzy Inference System (ANFIS), resulting in a robust hybrid controller. In the pursuit of optimizing the PEMFC system, the proposed GSO-ANFIS controller is subjected to rigorous testing under dynamic variations in both PEMFC temperature and load. Notably, PEMFCs, due to fluctuations in pressure and temperature, exhibit stochastic behaviour, forming a Gaussian surface. In this research, the popular Perturb and Observe (P&O) and Incremental Conductance methods are evaluated alongside the newly introduced GSO-ANFIS model. The proposed GSO-ANFIS controller outperforms its counterparts, showcasing an impressive accuracy level of 89.97%. In contrast, the Artificial Neural Network (ANN) achieves 80.33% accuracy, and the standalone ANFIS controller attains 86.5% accuracy. This disparity underscores the efficacy and potential of the novel hybrid approach, which not only adeptly handles the stochastic nature of PEMFCs but also significantly enhances accuracy in power optimization. This research not only contributes a valuable addition to the field of MPPT control but also offers a promising trajectory for the future development of efficient and reliable PEMFC systems.

Comparison of Incremental Conductance and Perturb and Observe Techniques of Maximum Power Point Tracking for PV Systems

The unceasing increase of the global energy call is linked with societies increasing awareness of the environmental effects of the extensive fossil fuels utilization. It has directed to the search for renewable energy sources, like as photovoltaic (PV) technology. The power from solar PV is connected directly to loads or fed to the grid. In general PV system is considered initially more expensive, however, it is the best suitable solution for standalone systems. With the developments in PV technologies, their applications increase rapidly, and grid-connected PV systems become popular. It indicates that PVs are more attractive to produce environmentally friendly electricity for various purposes. This research paper presents a comparison of incremental conductance (IC) and perturb and observe (P&O) in grid-connected PV. The study begins by providing a background on MPPT and the two specific techniques being evaluated. The P&O technique perturbs the voltage of PV array and notices the response in power output, while the IC method implements the slope of the power voltage curve to incrementally adjust the operating point. At the end, it was observed that the INC MPPT technique accomplished maximum power point quickly. Similarly, there was less fluctuation in I&C method than in P&O method. The paper concludes that the IC method is a more appropriate choice for MPPT in grid-connected PV systems due to its superior performance and robustness.