Maximum Power Point Tracking Technology Research Articles

Design of maximum power point energy storage and inverter for photovoltaic power generation

With the increasing tension of fossil energy in the world and the damage to the environment caused by the use of fossil energy, new energy led by solar energy has been paid much more attention by countries around the world. If the efficiency of solar energy is improved, it will bring breakthrough changes to the world’s energy structure. Based on the related applications of solar photovoltaic power generation, this paper designs an independent photovoltaic power generation system with energy storage device. In the form of DC/DC conversion, the system uses the maximum power point tracking technology of photovoltaic cells to realize the efficient use of solar energy during the charging process. The system inverts the DC voltage after the storage, realizing the transformation of DC voltage to 220 V AC voltage, to realize the utilization of energy in various aspects.

Разработка эффективного контроллера отслеживания точки максимальной мощности для фотоэлектрической системы

A method is presented to improve the performance of off-grid photovoltaic systems using a battery charge controller equipped with solar panel maximum power point tracking (MPPT) technology. Today, the MPPT controller is an effective technology that ensures the ideal functioning of solar panels by searching and selecting the optimal power point. The purpose of the study is to develop and evaluate the performance of a battery charge controller for solar panels based on the maximum power point tracking method. The proposed method uses a perturbation and observation algorithm and tracks the maximum power point based on the current and voltage output characteristics. A DC/DC buck converter was used in the study. The model of the proposed method was implemented in the Matlab Simulink software environment, and the efficiency of the operating algorithm was assessed. In addition, based on this model, an MPPT controller was developed, which was tested in real conditions. The reliability of the proposed method was also assessed. As a result of the study, the effectiveness of the developed system was revealed through a comparative analysis with a commercial battery charge controller

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

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.

Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system

The ability of the Maximum Power Point Tracking (MPPT) technology to prevent losses by stabilizing power fluctuations during severe weather conditions is critical in improving photovoltaic power generation systems. Overall system stability is improved by carefully tracing the maximum power point (MPP). This research focuses on improving MPPT performance in solar systems by employing the "Fuzzy Logic" control method. The simulation, which is run in MATLAB/Simulink, includes a detailed model of the entire system. The primary circuit is designed with a DC-DC Boost architecture and a single MOSFET transistor. The Fuzzy Logic Controller (FLC) unit in MATLAB/Simulink generates an output variable led by two input variables via the Fuzzy Logic Controller unit. The Fuzzy Logic Controller (FLC) unit generates an output variable led by two input variables in MATLAB/Simulink. The simulation model, called "Fuzzy Disturbance," combines Perturb & Observe with Fuzzy Logic and runs for 3 s. The results show an essential increase in system efficiency to 97%. The simulation results show that the suggested approach tracks MPP, reduces output power fluctuations, and improves system efficiency.

Solar Panel Prevention and Maintenance Indication Using IOT-Based MPPT System

Energy is absolutely crucial to the functioning of our modern society. Because of the limited reserves and negative impact on the environment, finding a replacement for conventional fuel or reducing its continuous consumption is crucial and essential. The functions of this IoT are sensor-based maintenance indication of solar panels and protection against theft. When dust accumulates on a solar panel, it reduces its efficiency. To solve this issue, an IoT-based maintenance warning system is implemented using a sensor placed in a strategic location, with an accompanying Android application designed to deter theft. A solar cell equipped with an MPPT (Maximum power point Tracking) technology is used to draw the most energy possible from the sun. Since this resistor detects light, the system's efficiency will improve. This preliminary model can be generalized to cover a wider range of systems.

Three-state Variable Step Perturbation Observation MPPT Algorithm Based on Power Prediction

In view of the application and research status of maximum power point tracking technology in photovoltaic power generation system, an improved disturbance observation MPPT method with variable step size and no oscillation at steady state is proposed in this paper. The method combines power prediction disturbance method and constant voltage method. Firstly, two reasons for power loss caused by traditional disturbance observation method are analyzed, which are disturbance misjudgment and steady state oscillation respectively. In order to reduce power loss caused by disturbance misjudgment and steady state oscillation, the three-point function sampling model based on Newton interpolation method is used to reduce disturbance misjudgment respectively, and a new variable step size factor is proposed to realize fast tracking of maximum power point through variable step size. And when the steady state is reached, the disturbance step size is immediately reduced to achieve a small shock output, so as to improve the efficiency of the whole system. Finally, the photovoltaic system model and experimental platform are built to track and simulate the maximum power point. The results show that the improved disturbance observation method can not only improve the disturbance misjudgment, but also achieve stable tracking of the maximum power point.

Principle and control strategy of a novel wave-to-wire system embedded ocean energy storage optimization

Fluctuation and unpredictability of wave power output affect the safe operation of the power grid, which greatly restricts the development of wave power generation. This paper firstly introduces the principle and control strategy of wave to wire (W2W) model, whose input is from wave energy conversion (WEC) system and output to the electrical power injected into grid for stable power. Then, an adapted particle swarm optimization (APSO) algorithm-based maximum power point tracking technology (APSO-MPPT) considering adaptive inertia weighting variables is presented for quick dynamic reaction. Comparisons are made with the traditional constant voltage method, INC method, P&Q method, and PSO algorithm, the APSO-MPPT method can reduce the tracking time by 9.3% less than the quickest traditional. Secondly, a K–SOC curve algorithm-based hybrid energy storage system (KSC-HESS) optimization is conducted for smoothing the wave power output, with real time control performance. Simulations under output power fluctuation, load pulsation and load unbalancing situations are occupied for demonstrating high performance for the characteristics of the proposed KSC-HESS, in both reducing high-frequency burden for super-capacitors and protecting batteries from over voltage. Furthermore, results of prototype experiment verified the effectiveness with better efficiency by adapting the proposed APSO-MPPT and KSC-HESS for the proposed W2W system. Analysis shows the proposed W2W approach has comparative advantages of control complexity and high reliability for renewable power output.

A Framework of Reduced Sensor Rooftop SPV System Using Parabolic Curve Fitting MPPT Technology for Household Consumers

In this paper, a novel Parabolic Curve-fitting based Hill Climbing (PCHC) MPPT (Maximum Power Point Tracking) algorithm is developed to extract maximum power from solar photovoltaic (PV) panels under dynamic environmental conditions for household consumers. Moreover, this developed PCHC algorithm is integrated with a novel reduced sensor-based approach, where only a single current sensor is used to complete the solar-powered battery charging process. The ease of implementation with a simple controller and reduced sensor approach makes it economical for residential rooftop SPV. In the proposed PCHC methodology, two consecutive points on the Power-Voltage (P-V) characteristic of PV are used to detect the Maximum point region quickly. Moreover, the parabolic nature of the P-V characteristic near the Maximum Power Point (MPP) zone is explored to calculate the approx value of voltage corresponding to the optimum power. Here in every iteration, the size of the perturbation in duty is reduced by fifty percent. It overcomes the trade-off between tracking speed and oscillations near the MPP of conventional MPPT techniques such as perturb and observe, incremental conductance. These oscillations affect the connected consumers’ load working efficiency and lifetime. Moreover, it also improves the dynamic performance by taking different step sizes for sudden and slow changes in irradiation levels. The boost converter has been used to realize the performance of the proposed PCHC MPPT technique. The performance of the PCHC MPPT algorithm has been validated on different types of irradiation patterns and obtained results that fulfill the motive of the work.

Back Propagation Neural Network PO-based Grid Connected Dual PV Management and Reliability Improvement

A single-phase Photovoltaic (PV) system that is based on an Maximum Power Point Tracking (MPPT) and use the perturb and observe algorithm to track the extreme power point. As a consequence of interactions between a variety of semiconductors and changing loads, the input source suffers from power quality concerns such as harmonic distortion, voltage sags, and voltage spikes. These issues are caused by fluctuating loads. As a method for enhancing the excellence of the electricity, the Grid-connected PV and Neural Network System was suggested as a solution. The Perturb and observe algorithm (P&O)-based MPPT technology offers a solution to the issue of partial shadowing, in addition to other unbalanced components that are problematic for PV arrays. Due to the fact that each PV cell in the system’s two panels is identical, it

Improvement of Voltage Collapse of MPPT Single-stage Photovoltaic Grid-connected based on Conductance Increment Method

In order to make the supply of resources meet the rapidly developing world situation, it is of great practical significance to further develop and apply renewable resources. PV power system has many advantages, such as cleanness, environmental protection, high efficiency, etc., and shows great application potential in the field of new energy. At present, solar cells are easily affected by external factors such as light intensity, temperature, etc., and can't work at the maximum power point continuously and stably, resulting in low power generation efficiency. Therefore, the introduction of Maximum power point tracking (MPPT) technology is particularly important. In this paper, a variable step conductance increment method is proposed, which can identify the current distance from the maximum power point by observing algorithm, change the tracking step and quickly identify the maximum power point, so as to solve the voltage collapse problem of single-stage photovoltaic grid-connection. Simulation results show that the new algorithm can optimize both dynamic response and steady-state oscillation, and improve the accuracy and speed of MPPT.

Designing a solar system that improved the solar system's performance in instances of partial shadowing

The great and decisive influence of the decision of nature or the natural factors of the earth, especially the strength of the solar radiation emitted by the sun and the temperature, remains an important condition and a factor that must be when talking about the exploitation or use of solar energy. From the beginning of the manufacture of solar cells to this day, the solar cell, which is the smallest component of the solar module or structure, has faced many challenges and difficulties, including design, manufacturing and cost challenges in addition to natural and abnormal factors. Among those challenges are natural and unnatural factors such as partial shading or full shading of the solar cell or the solar cells within the solar structure in the solar system, which directly affects the process of producing solar cells from electrical energy. As a result, these natural and unnatural challenges prevent the optimal and full exploitation of solar energy. Through this research paper, a new strategy was found, which is a simple electronic system to enhance the performance of solar cell production within the solar structure in the solar system, and this strategy includes the establishment of a new existing solar system. On a boosting system linked with the solar structure through which this electronic system can be connected to be an integral part of a solar system, to enhance the solar system's effectiveness in the production of electrical energy. The new solar system works in any conditions (natural and abnormal) and any type of shading (partial or total) for the solar panel or structure, through this new system, The quantity of electrical energy generated by the new solar system is equal to twice the energy of solar cells or arrays solar system within the solar structure under normal circumstances. In addition, the use of solar radiation tracking techniques is not important, as it is the strategic comparison used to determine the maximum power point tracking (MPPT) technology under various circumstances to produce good solar energy in poor lighting conditions by concentrating on the impacts of shadowing on the array of solar photovoltaic panels, a strategy that did not succeed in maintaining the productivity of the solar array from electrical energy in natural conditions, but the proposed model or the new methodological strategy helped to maintain the productivity of the solar cells or solar array from electrical energy in addition to increasing the production of electrical energy under the bad exposure conditions of the solar system. The proposed model is chosen in view of the fact that it may be able to reproduce different ranges of non-standard PV frames standardized by a new solar system with good multidimensional uses. The study may be presented here as a suitable source for future business of power generation (PV).

MPPT implementation and simulation using developed P&O algorithm for photovoltaic system concerning efficiency

The great development witnessed by investments in renewable energy has made it the focus of researchers’ attention in order to increase its efficiency. This is due to the increase in demand for electrical energy due to rapid technological growth, increase in population numbers, and high fuel prices that are used in the production of traditional electrical energy, but it suffers from a problem that is greatly affected by two factors, namely, the change in the intensity of solar irradiation and temperature, which makes its electrical characteristics non-linear, which causes a decrease in its efficiency. To address the efficiency problem, the researchers developed several techniques for tracking the MPP point and extracting the maximum energy from the solar panels under various measurement conditions. Maximum power point tracking technology (MPPT) technology is the most widely used technology in solar energy systems. In this article, MPPT technology is simulated using MATLAB/Simulink for the purposes of extracting maximum power and managing the duty cycle of a DC-DC buck converter. The performance of the photovoltaic system under various irradiance fluctuations and settings of constant temperature could well be determined using simulation results. Under standard and varied test settings, allowing the inverter to convert over 99% of the electricity provided by the solar panels.

A novel maximum power point tracking technique with improved particle swarm optimization for proton exchange membrane fuel cell

The performance of proton exchange membrane fuel cell (PEMFC) can be significantly affected by its operating conditions, i.e. the temperature, membrane water content. Aimed at maximizing the performance of PEMFC, maximum power point tracking (MPPT) technology plays an important role in PEMFC system. Most traditional MPPT algorithms will generate steady-state oscillations, which result in power loss and damage to PEMFC. In addition, most MPPT controllers based on intelligent algorithms need to use PID to track the MPP, which increases the complexity of the controller and makes the tracking result strongly depend on the PID gain. To overcome steady-state oscillation and reduce the complexity of the MPPT controller, a MPPT controller based adaptive particle swarm optimization algorithm (APSO) without a PID controller is developed in this paper. The performance of the presented algorithm is investigated under three cases including stable operating condition, temperature change and membrane water content variation, and compared with traditional particle swarm optimization algorithm (PSO) and perturbation and observation (P&O) method. The obtained results indicate that APSO has faster tracking speed and smaller search oscillation than PSO, and has better stability than P&O. Moreover, the results demonstrate that by using duty cycle as decision variable, simple design of MPPT control system can be obtained, which shows great superiority over PID controller. This not only enables real-time online tracking, but also reduces hardware manufacturing costs.

A Systematic Review and Analysis of MPPT Techniques for TEG Systems Under Nonuniform Temperature Distribution

As a waste heat recovery power generation technology, the thermoelectric generation (TEG) system is popular and promising for its high reliability and environmental benefits. However, because of its low conversion efficiency, it has not been in large-scale use. To raise the efficiency of the TEG system, maximum power point tracking (MPPT) techniques are effective ways to gain the maximum power of the TEG system. But in practical engineering scene, TEG usually works under nonuniform temperature distribution (NTD) conditions; this will bring some difficulties on MPPT controlling, such as local maximum power point (LMPP) and oscillations around the maximum power point (MPP). For this reason, many intelligent MPPT algorithms have been emerged to solve aforementioned problems. In this article, the mathematical model and NTD condition will be introduced. Then, the latest research on classical and intelligent MPPT technologies will be reviewed, which includes a comparison of complexity, economy, efficiency, adaptive ability, and other aspects of these methods, in which researchers can obtain information in related fields.

Design and control of Micro-Grid fed by Renewable Energy Generating Sources

This paper presents a half breed sustainable age framework at an isolated region empowered from sun powered and wind energy sources. DFIG (Doubly Induction Generator) outfitted with MPPT (Maximum Power Point Tracking Technology) is used to remove wind energy. A glasslike sun situated photovoltaic (PV) system is used to change over sun oriented power which is overseen at the standard DC transport of DFIG. The sun based power is supported through DC help converter which is moreover outfitted with MPPT computation to isolate most outrageous sun situated energy. A battery bank is related at the ordinary DC transport of the DFIG which goes about as support storing for exchange of energy. The system is planned for complete customized job taking idea of the multitude of practical circumstances. The structure is similarly given a game plan of external power support for battery charging. The voltage and repeat are controlled through a modified underhanded vector control of the load side converter which is combined with hang ascribes. It changes the repeat set point considering the energy level of the battery. The structure is exhibited in Sim-Power System apparatus reserve of MATLAB and its execution is reproduced under moving circumstances for example unviability of wind or sun based energy, inconsistent and nonlinear weights has been shown. Keywords Renewable energy sources, solar power generation, wind power, Hybrid system, MPPT Controller, micro grid.

New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Genetic Algorithm Optimization

Solar energy is highly dependent on climatic conditions and swings sporadically, making photovoltaic integration into power systems difficult. In order to improve the design of power infrastructure and the successful deployment of decentralized and renewable energy sources, a new paradigm for the energy supply chain is established, leading to the creation of microgrids. Intelligence should be introduced at all levels of the grid, and it should act over a long period. This article introduces new research tools and methodologies to help utility engineers better understand the grid's potential impact from these new power sources. Therefore, this research proposes a novel PV solar system control method. The strategy is an innovative Maximum Power Point Tracking (MPPT) technology for solar systems. Designing an effective controller capable of lowering Power Stress inside the PV System is critical in this respect. In this research, the Proportional Integral (PI) controller has been utilized in conjunction with a heuristic technique termed Genetic Algorithm (GA) in order to regulate and stabilize the MPP of the power supplied by the PV system. Then the GA technique has been utilized to identify the ideal settings, based on the four performance indices of Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time Absolute Error (ITAE), and Integrated Time Squared Error (ITSE) (Kp, Ki). The simulation results show that the PV system can successfully monitor the required MPP.