Maximum Power Point Tracking Method Research Articles

Dynamic model and robust control for the PEM fuel cell systems

In response to the escalating challenges posed by climate change, the global energy sector has witnessed a paradigm shift towards sustainable alternatives. The promising fuel cell technology known as the proton exchange membrane fuel cell (PEMFC) has found widespread use in a variety of mobile and stationary applications. This paper presents a super-twisting sliding mode (STSM) control for maximum power point tracking (MPPT) on the proton exchange membrane fuel cell (PEMFC) system incorporated floating interleaved boost converter (FIBC). This work aims to extract the maximum power from the PEMFC by means of robust control in conjunction with FIBC to improve current ripple. The proposed controller is designed for the PEMFC system by combining the STSM and MPPT methods. The designed MPPT control tracks down the maximum power point of the system, and the corresponding current values act as the reference values for the STSM controller. The results show that incorporating the PEMFC with the FIBC can result in current ripple reduction when compared with a traditional interleaved boost converter (IBC). The obtained results demonstrate the capability of the PEMFC closed-loop control system to maintain the system's robustness under fuel cell parameter variations.

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

An Improved Perturb and Observe MPPT for Photovoltaic Systems using Fuzzy Step Size

Photovoltaic (PV) systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The output power of these systems relies on solar cell radiation and temperature. To mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. The Maximum Power Point Tracking (MPPT) algorithm is employed to optimize power extraction from PV systems. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely Perturb and Observe (P&O) and Fuzzy Logic Controller (FLC), have been modified. This research work aims to simulate and validate the fuzzy step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using Matlab/Simulink™ for efficient power tracking in PV systems.

Investigating the Effectiveness of Maximum Power Point Tracking (MPPT) with Perturb and Observe (P&O) Algorithm in Solar Power Battery Charging System

Solar energy has emerged as one of the most promising and crucial renewable energy sources in our efforts to reduce reliance on fossil fuels and mitigate their adverse impact on the environment. One of the primary applications of solar energy is in the solar power battery charging system. This system harnesses solar energy emitted by the sun through solar panels to charge batteries used as energy storage. At the heart of the solar power battery charging system lies the technology of Maximum Power Point Tracking (MPPT). MPPT is a method used to achieve maximum efficiency from solar panels by adjusting their voltage and current to ensure they constantly operate at their maximum power point, irrespective of varying light conditions. In this article, the solar power battery charging system with the use of the MPPT method employing the Perturb and Observe (P&O) algorithm will be explored. The proposed of modeling the solar battery charging system with the implementation of a DC-DC buck converter and lead-acid battery incorporated with P&O algorithm will be elaborated through MATLAB/Simulink simulation software. Additionally, the performance analysis of the system will be investigated by considering the state of charge (SOC), power (W), current (A), and voltage (V) variables. The simulation results indicate the effectiveness of the proposed P&O algorithm in maintaining the panel’s operating maximum power in multi-stage charging system.

A comprehensive comparison of the two MPPT techniques (P&O, SMC) for photovoltaic systems

This paper aims to study the behavior of different maximum power point tracking (MPPT) techniques applied to PV systems. In this work, we evaluate and compare perturbation and observation (P&O) and sliding mode control (SMC) techniques. a DC/DC boost converter is used between the PV system and load to transfer the maximum possible power. the conventional MPPT methods Perturb and Observe (P&O) cannot detect and track the highest peak. Thus, this causes an important loss of power, so a technique is suggested sliding mode control, which offers many benefits such as durability against parameter changes, minimal current output distortion, and excellent reference tracking. The simulation results shown in MATLAB confirm that the sliding mode control method allows to have a faster response compared to P&O, to reduce the steady-state fluctuations and better track the maximum power point with fewer power losses.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

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.

Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator

Introduction. In this paper, to maximize energy transmission in wind power system, various Maximum Power Point Tracking (MPPT) approaches are available. Among these techniques, we have proposed the one based on typical fuzzy logic. Despite the somewhat reduced performance of fuzzy MPPT. For a number of reasons, fuzzy MPPT can replace conventional optimization techniques. In practice, the effectiveness of conventional MPPT methods depends mainly on the accuracy of the information given and the wind speed or knowledge of the aerodynamic properties of the wind system. Novelty. Our new MPPT for monitoring the maximum power point has been proposed. We developed an algorithm to improve control performance and govern the stator’s developed active and reactive power using the typical fuzzy logic 2 and enable robust control of a grid-connected, doubly fed induction generator. Purpose. MPPT which implies the wind turbine’s rotating speed should be modified in real time to capture the most wind energy, is necessary to achieve high efficiency for wind energy conversion, according to the aerodynamic characteristics of the wind turbine. Methods. Developing a mathematical model for a wind energy production system is complex, can be strongly affected by wind variation and is a non-linear problem. Thanks to these characteristics, thus, the Lyapunov technique is combined with a sliding mode control to ensure overall asymptotic stability and robustness with regard to parametric fluctuations in order to accomplish this goal. We contrasted our fuzzy type-2 algorithm’s performance with that of the fuzzy type-1 and Perturbation & Observation (P&O) suggested in the literature. Practical value. The simulation results demonstrate that the control performance is satisfactory when using the fuzzy logic technique. From these results, it can be said for the optimization of energy conversion in wind systems, the fuzzy type-2 technique may offer a workable option. Since it presents a great possibility to avoid problems either technical or economics linked to conventional strategies.

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.

Simulation of Maximum Power Point Tracking with Fuzzy Logic Control Method on Solar Panels Using MATLAB

One of the newest sources energy is the sun. To limit the usage of fossil fuels, solar power facilities that harness sunlight are beginning to be created. Currently, research on solar panels is increasingly being carried out. Because solar panels are an unlimited source of energy. And solar panels can reduce exhaust emissions from conventional vehicles by 92%. However, the performance of solar panels is strongly influenced by several factors such as sunlight intensity and ambient temperature. So that solar panels can reach the maximum power point, the Maximum power point tracking (MPPT) method is employed to maximize the performance of solar panels. In this research, an mppt system will be created using fuzzy logic control in matlab simulink. Fuzzy logic control is a method that can control the system against load changes. To analyze this fuzzy logic-based mppt system, simulations are carried out using matlab simulink. Simulink is software used to model, simulate, and analyze dynamic systems. From the simulations that have been carried out, solar panels without mppt have a duty cycle change time of 0.1 seconds while with mppt the duty cycle change is 0.7 seconds, where the duty cycle change time without mppt is faster but the operating value is unstable while power tracking with mppt the duty cycle change time is longer but the operating value is more stable.

Design and Simulation of A Single Current Sensor Maximum Power Point Tracker for Solar Hydrogen System

Renewable energy sources have attracted a lot of research interest in the past decade. Among these, solar energy is regarded as one of the promising energy sources and has been deployed worldwide with the installed capacity continually increasing. Solar-hydrogen systems have been under research for more than two decades. In such system, maximum power point tracking (MPPT) plays an important role to deliver maximum power from solar panels to the electrolyser. In this paper, two simple but effective MPPT methods are developed and evaluated. Differed from most of the existing methods, these methods are only reliant on a single current sensor input to locate the maximum power point of the solar panel.

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

An adaptive fuzzy maximum power point tracking for PV systems by a mutant gray wolf optimization algorithm

Abstract An innovative and adaptive maximum power point tracking (MPPT) based on a fuzzy controller is proposed in this article. The proposed controller is optimized by the mutant gray wolf optimization (MGWO) algorithm, and the objective function uses an integral squared time squared error (ISTSE) index. To indicate the efficiency of the proposed MPPT algorithm (MGWO-AF-MPPT), the simulations for several irradiation conditions are modeled in MATLAB. In addition, two fair comparisons are performed between the simulation results of the proposed MPPT method with an adaptive fuzzy controller that optimized with particle swarm optimization (PSO-AF-MPPT), as well as a Perturbation and Observation method (P&O-MPPT). The comparison results illustrate that the amount of generated electrical power from the PV system for all adaptive MPPT systems is almost close to each other, indicating the high accuracy of both fuzzy-based controllers. However, the PV power generation of the proposed MGWO-AF-MPPT is higher, and also its ripple is lower than PSO-AF-MPPT. Note that, the calculated total harmonic distortion (THD) for the P&O-MPPT, PSO-AF-MPPT, and the proposed MGWO-AF-MPPT methods are calculated at 3.4%, 2.3%, and 2.1%, respectively. A lower THD indicates better power quality generated by the PV system employing the MGWO-AF-MPPT method.

An optimization power method for Photovoltaic array by tracking Maximum Average Current through Diode of Boost Converter

In this paper, a method to optimize the power delivered by the Photovoltaic array (PV array) is presented by tracking the Maximum Average Current (MAC) passing through the diode of DC/DC boost converter. The main difference between the proposed method and the other Maximum Power Point Tracking (MPPT) methods is that instead of tracking maximum power point directly on PV array, this method enables to search the maximum power of PV array by tracking the maximum power output of the DC/DC converter with only one current sensor. An hybrid power system, which includes a PV array, a DC/DC boost converter, an ideal battery and a resistor, is built and simulated in MATLAB/Simulink. The comparison between analytical and simulated results has proven that this method could be applied under various irradiation and photovoltaic temperature conditions.

Modelling and Power Control of Wind Turbine Driving DFIG connected to the Utility Grid

In this paper, the modelling and power transfer control of the variable speed wind energy system are presented. Firstly, a Maximum Power Point Tracking (MPPT) method is developed in order to maximize the energy generation of wind system based on doubly fed induction generator (DFIG). Then, the control of both Rotor Side Converter (RSC) and Grid Side Converter (GSC) is studied and the design of a multivariable selective filter is presented. Finally, simulation of a 850 kW doubly fed induction generator wind system was performed in the Matlab / Simulink / SimPowerSystems environment

Numerical Experimental Validation of a Proposed MPPT Algorithm with Dynamic Hysteresis for PV Systems

In the last decades, the renewable energies have had a highly positive effect on the daily life. This due to the necessity of reducing the common fuel energies that have negatively impacted the environment. Among all the available renewable energies, the solar energy is one of the most common. This energy is obtained through Photovoltaic (PV) systems. Thus, in previous years, new techniques of Maximum Power Point Tracking (MPPT) to raise the quality of the energy provided by PV panels have been proposed. Since the energy provided by the PV cells depends on the external environmental conditions, as temperature or sun irradiance, the MPPT methods should be adequate to deal with these external changes by maintaining the desired power level. Hence, this paper proposes a recent hysteretic dynamic technique to extract the maximum power from a PV panel array by employing a Boost DC/DC converter to supply energy to an inductive load. Here, a comparative study between the results obtained with the well known Perturb and Observer (P&O) algorithm and by using our dynamic hysteretic MPPT method is analyzed, specifically when the PV panel is submitted to fast variations in temperature and irradiance. It will be proved through numerical experiments realized in MatLab/Simulink that our hysteretic MPPT algorithm provides a better achievement of the maximum power of the PV panel in comparison to the conventional Perturb and Observer method.

A New Design and Embedded Implementation of a Low‐Cost Maximum Power Point Tracking Charge Controller for Stand‐Alone Photovoltaic Systems

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method.

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.

Fuzzy Logic Based Adaptive P&O MPPT Control of a DC-DC Boost Converter Used in a Wind Energy Conversion System with a Permanent Magnet Synchronous Generator

In this paper, we present a Maximum Power Point Tracking (MPPT) algorithm employing Perturb and Observe (P&O) using Fuzzy Logic (FL) control to overcome limitations associated with the conventional P&O MPPT method in order to improve transient response and reduce steady-state terminal voltage oscillations. In particular, the FL approach was applied to define the perturbation amplitude for the P&O MPPT algorithm, according to the ratio between the incremental power and incremental voltage in the input of a DC-DC boost converter. The approach was a variable speed wind power conversion chain based on the permanent magnet synchronous machine. The MPPT control continuously adjusts the duty cycle of the DC-DC converter to maintain the wind turbine at the maximum power point. MATLAB-SIMULINK environment is used to simulate the entire system demonstrating the extraction of the maximum available power from the wind turbine under a varying wind speed.

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading