Intelligent Maximum Power Point Tracking Research Articles

Comparative Study of MPPT Techniques in Solar PV Systems: A Comprehensive Review

Abstract: Maximum power point tracking(MPPT) is a very important technique which is employed in photovoltaic systems to extract maximum available power from the Photovoltaic panels under varying operating conditions. This paper presents a comparative study of different MPPT techniques, including traditional methods like Perturb and Observe (P&O), Incremental Conductance (Inc Con), Constant voltage method, as well as intelligent methods like Fibonacci algorithm-based search MPPT and Particle Swarm Optimization (PSO) Technique. The advantages and disadvantages of each technique are discussed in detail, along with their suitability for different applications. The paper concludes by highlighting the limitations of existing techniques and proposing a problem statement for developing a novel MPPT technique that addresses these limitations. Traditional MPPT techniques like Perturb and Observe , Incremental Conductance, and constant voltage method are straightforward to deploy and computationally economical. However, they can encounter issues such as being trapped in local maxima and exhibiting slower tracking rates during swift changes in irradiance. Intelligent MPPT techniques, such as Fibonacci algorithm-based search MPPT, and Particle Swarm Optimization (PSO) Technique are more adaptable to complex operating conditions and have faster tracking speeds, but they are also more computationally complex and expensive to implement. The limitations of existing MPPT techniques include sensitivity to partial shading conditions, limited efficiency under dynamic irradiance conditions, and the requirement for accurate system parameters. To address these limitations, a novel MPPT technique should be developed that is efficient under partial shading and dynamic irradiance conditions, robust to inaccurate system parameters, and has low computational complexity and implementation cost.

Intelligent MPPT control for SEPIC-Luo converter in grid tied photovoltaic system

Grid connected solar photovoltaic (SPV) systems are becoming more and more common due to steadily rising energy demand. The advantages of photovoltaic power generation, such as its eco-friendliness, low maintenance requirements, and lack of noise, are making it as a significant renewable energy source (RES). This framework presents the modeling and control design of PV grid tied system implemented with integrated single ended primary inductance (SEPIC) Luo converter. The main goal of this work includes investigating solar PV system behaviour and creating an effective grid connected solar power. Solar PV module tracks maximum power, with an aid of chaotic cascaded fuzzy a maximum power point tracking (MPPT) has developed. The DC voltage obtained is fed to 1Φ voltage source inverter (VSI) for conversion of AC voltage. In comparison to typical PWM control, the spectrum performance of the examined voltages is improved by adjusting the nominal duty cycle of main switch of SEPIC-Luo converter. So that PV output impedance is equivalent to DC-DC converter's input resistance. Finally, the obtained AC voltage is supplied to 1Φ grid for further applications. With less THD, an efficiency of 96% is achieved when the implementation of the suggested system is carried out using MATLAB/Simulink.

Long Short Term Memory Tracker‐Based Modified DC‐DC Converter for Power Quality Improvement in Grid‐PV Systems under Uniform and Partial Shading Environments

In previous era, coal and oil like non‐renewable resources were mostly used for power generation, thus causing a severe environmental problem as well as high economic. An intelligent maximum power point tracking (MPPT) system was proposed to overwhelm the above‐mentioned impacts, which tracks the maximum generation of photovoltaic (PV) under various atmospheric conditions. Moreover, a modified DC‐DC converter was introduced to low inrush current and switching oscillation. The proposed model working process was split into three steps: first, design the normal PV with a grid‐integrated power system. Second, create a dataset with the aid of a designed PV model, and finally, design an intelligent MPPT controller based on that dataset. The difference among the MPPT and PV voltage was fed to an optimal controller that generates an appropriate pulse signal of the modified DC‐DC converter. The proposed intelligent tracking system was analyzed under various circumstances like static, dynamic and combined shading conditions. The proposed model offers 99.5% efficiency and very low harmonics values like 0.15% current total harmonic distortion (THD) and 0.46% voltage THD. The outcome verifies that the intelligent MPPT approach performs well than that of the currently used controllers in solar power generation and conditioning systems.

Modeling of intelligent controllers for solar photovoltaic system under varying irradiation conditions

The increasing demand for solar renewable energy resources, driven by the global energy crisis and the depletion of conventional energy sources, has underscored the importance of harnessing solar energy. Solar photovoltaic (PV) systems, however, exhibit nonlinear output power due to their weather-dependent nature, impacting overall system efficiency. This study focuses on the development and comparative analysis of three intelligent Maximum Power Point Tracking (MPPT) controllers using the MATLAB Simulink. The controllers employ distinct methodologies, namely, Artificial Neural Networks (ANN), Adaptive Neural and Fuzzy Inference System (ANFIS), and Fuzzy Logic Controller (FLC). The results demonstrate that ANFIS achieved the highest accuracy at 99.50%, surpassing ANN and FLC with accuracies of 97.04% and 98.50%, respectively, thus establishing ANFIS as the superior MPPT controller. Additionally, the positives and negatives of all three MPPT-based algorithms are also compared in this work.

Enhancing photovoltaic system modeling and control under partial and complex shading conditions using a robust hybrid DE-FFNN MPPT strategy

Improving efficiency, reliability, and extending the life of Photovoltaic (PV) systems are some essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control system. In this study, an intelligent MPPT approach based on the Differential Evolution (DE) algorithm, and an adaptive Feed-Forward Neural Network (FFNN) is developed to address various issues of the most commonly used MPPT algorithms that affect the performance of PV systems including, power generation efficiency, tracking time, and instabilities around the Global Optima (GO). Consequently, in order to determine the suitable hyper-parameters’ values for the optimization and training procedure, the suggested hybrid technique is evaluated in the first part of this study for a variety of the FFNN configuration’s possibilities. Afterwards, the best obtained neural model is implemented as an MPPT controller, and a performance appraisal has been made between the DE algorithm based Feed-Forward Neural Network (DE-FFNN) and the most efficient utilized MPPT techniques. The observed results in the optimization and training phases show that our proposed strategy with 11 neurons in the hidden layer presents the most optimal neural model with a Mean Square Error (MSE) of 1.4924 in 139.8613 s in the optimization phase, and 2.57e−14 in the training phase. Subsequently, the created DE-FFNN model based-MPPT with the proposed configuration illustrates excellent performance compared to the applied MPPT techniques with a tracking speed between 0.0237 and 0.0106 s, a remarkable reduction in instability, and a power efficiency that can go up to 99.9971% in four different irradiance scenarios.

A state of the art a hybrid intelligent strategies of maximum power point tracking: a systematic contemporary

Renewable energy sources are among the best substitute sources to fossil fuel due to it is very suitable for mitigating global warming; solar energy is considered the main causes of renewable energy, and solar photovoltaic (PV) generation systems have gained importance worldwide due to several characteristics, including cheap maintenance, low noise, and low fuel costs. However, one of the most difficult challenges facing solar energy systems is changing weather circumstances. The robust control approach stabilizes the PV system's output and maximizes harvested energy. In this study, several maximum power point tracking (MPPT) performances have been presented. Among them, artificial intelligence (AI) based on MPPT methods demonstrates the ability to capture the MPP point. There are several ways to apply AI to MPPT, and this paper presented various intelligent MPPT methods in detail with their benefits and drawbacks and comparison among them to select which technique is suitable and can be used to change weather conditions with<strong> </strong>horse optimization method (HOM) plus neural artificial system (NAS).

Intelligent maximum power point tracking for coastal photovoltaic system concerning the corrosion and aging of modules

Due to the corrosion and aging caused by the special oceanic environment, the characteristic of coastal photovoltaic (PV) system significantly drift after years of operation. In this study, the maximum power point tracking (MPPT) problem for coastal PV system is addressed and a novel MPPT methodology based on deep neural network (DNN) integrated with the corrosion evaluation index (CE-index) and dynamic training-sample (DTS) mechanism is developed. To be specific, the detailed effect of corrosion and aging for the PV modules installed in coastal areas is comprehensively analysed, and a composite indicator for evaluating the PV parameter drift, namely CE-index, is proposed. Then, a novel DNN-based offline MPPT methodology for the large-scale coastal PV system is developed, in which the DTS mechanism is also introduced for overcoming the effect caused by PV module corrosion and aging phenomenon. Finally, the optimal length of DTS for different degrees of CE-index is comprehensively verified by case studies. Experimental result shows that the developed DNN-based MPPT methodology can accurately forecast the maximum power point (MPP) voltage for large-scale coastal PV-system with robust performance, and cooperation of the developed DTS-mechanism and CE-index corrosion evaluation strategy can also effectively overcome the disturbance caused by the harsh oceanic environment.

Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.

Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.

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.

Fuzzy logic concept based MPPT technique for standalone PV system

This paper presents a study of an Intelligent Maximum Power Point Tracking (IMPPT) system for the Standalone PV system. The proposed MPPT technique is based on a fuzzy control concept (FLC). FLC is easy to design due it doesn’t require precise system knowledge and work well with the nonlinear systems; the obtained results were compared with the famous MPPT method (P&O). According to the obtained simulation results, the proposed method gave good results compared to the P&O technique, these results are shown by the low oscillation and rapid response of the proposed method, which provides the maximum transmission of power to the load.

Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators

Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

Increase of the photovoltaic resources power using multi-input DC–DC converter and model-based MPPT algorithm

Partial shading conditions decrease the photovoltaic string’s output power because of the bypass diodes operation. Also, the extraction of maximum available power from photovoltaic systems has become important and challenging. To overcome partial shading conditions and improve power prediction, intelligent maximum power point tracking techniques can be used. These methods must track the maximum power point voltage under different environmental situations to maximise the photovoltaic systems’ efficiency. In this paper, a model-based maximum power point tracking algorithm is accomplished through a multi-input interleaved boost DC–DC converter, to increase photovoltaic systems’ output power and efficiency. To show the better performance of the proposed method, a comparison with P&O and incremental conductance techniques is presented. The simulation results illustrate a remarkable increment in the derived power from the photovoltaic string by interfaced the maximum power point tracking algorithm with the interleaved converter compared to the traditional single-converter structure.

An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm

Because the traditional maximum power point tracking (MPPT) of photovoltaic (PV) array is easy to fail in complex environment, and the existing intelligent optimization algorithms have shortcomings such as slow convergence speed, low convergence precision and poor stability. Therefore, this paper proposes a new intelligent optimization algorithm MPPT tracking method. Based on mayfly intelligent optimization algorithm, this method can track the maximum power point (MPP) of PV system by two populations, which can quickly and accurately track MPP of PV system under complex environmental conditions. By using Matlab/Simulink simulation platform, the method was studied under uniform illumination and non-uniform illumination respectively, and its effectiveness was verified. Compared with the particle swarm optimization algorithm (PSO), its superiority is verified.

Investigation of Adaptive Intelligent MPPT Algorithm for a Low-cost IoT Enabled Standalone PV System

ABSTRACT This paper explicates a standalone solar photovoltaic system design to track maximum power by utilising intelligent adaptive control algorithms. Conventional MPPT algorithms are not efficient enough to follow the maximum power variable irradiation and temperature conditions. Therefore, an intelligent algorithm has been required to extract the maximum power in a standalone PV system. The present paper incorporates adaptive intelligent maximum power point tracking (MPPT) method adaptive neuro-fuzzy inference system (ANFIS) techniques to extract maximum voltage and power. The fuzzy logic controller (FLC) has been implemented to analyse the performance compared to the ANFIS method. Because of the utilisation of conventional techniques, the point of maximum power gets oscillated in a low irradiance level and the values move between forward and backwards but do not have a fixed value. The used ANFIS method takes all the possibility values from 0 to 1, increasing efficiency. The efficiency of the ANFIS-based MPPT method is 90% more accurate than those of other conventional methods, which has been presented in the paper. For the remote monitoring of the obtained voltage, current and power, internet of things (IoT) features have been incorporated into the considered standalone PV system. The presented standalone PV system has been experimentally verified and validated for the efficiency evaluation of the proposed ANFIS algorithm.

Dynamic Model and Intelligent Maximum Power Point Tracking Approach for Large‐Scale Building‐Integrated Photovoltaic System

Due to the complex surrounding environments, unpredictable occupants’ behaviors, and the photovoltaic (PV) module degradation after years of operation, the fast and accurate maximum power point tracking (MPPT) of building‐integrated photovoltaic (BIPV) system is very difficult. Herein, the MPPT of a huge BIPV system is modeled as a large‐scale global optimization (LSGO) problem, in which all the variables are proven to be separable. Then, a novel differential evolution (DE) algorithm with the “DE/Current‐to‐Tbest/Current‐to‐Gbest/1” mutation operation (DE‐TG1) is developed to optimize the aforementioned separable LSGO−MPPT model. In addition, the problem of PV module degradation and its effect on the LSGO‐MPPT model are analyzed in detail. To reduce the influence of PV degradation on MPPT accuracy, the dynamic LSGO−MPPT model is developed by introducing the annual drift rates into the proposed LSGO−MPPT model. Finally, the developed methodologies are comprehensively tested by simulation experiments. Experimental results show that the developed DE‐TG1 algorithm significantly outperforms the other state‐of‐the‐art algorithms on separable LSGO problems. In addition, result of the experiments also indicates that with the cooperation of the DE‐TG1 algorithm and the dynamic LSGO−MPPT model, the effective MPPT control of a large‐scale BIPV system can be obtained with accurate and robust performance.

A Fast-Tracking Hybrid MPPT Based on Surface-Based Polynomial Fitting and P&O Methods for Solar PV under Partial Shaded Conditions

The efficiency of photovoltaic (PV) systems depends directly on solar irradiation, so drastic variations in solar exposure will undoubtedly move its maximum power point (MPP). Furthermore, the presence of partial shading conditions (PSCs) generates local maximum power points (LMPPs) and one global maximum power point (GMPP) in the P-V characteristic curve. Therefore, a proper maximum power point tracking (MPPT) technique is crucial to increase PV system efficiency. There are classical, intelligent, optimal, and hybrid MPPT techniques; this paper presents a novel hybrid MPPT technique that combines Surface-Based Polynomial Fitting (SPF) and Perturbation and Observation (P&O) for solar PV generation under PSCs. The development of the experimental PV system has two stages: (i) Modeling the PV array with the DC-DC boost converter using a real-time and high-speed simulator (PLECS RT Box), (ii) and implementing the proposed GMPPT algorithm with the double-loop controller of the DC-DC boost converter in a commercial low-priced digital signal controller (DSC). According to the simulation and the experimental results, the suggested hybrid algorithm is effective at tracking the GMPP under both uniform and nonuniform irradiance conditions in six scenarios: (i) system start-up, (ii) uniform irradiance variations, (iii) sharp change of the (PSCs), (iv) multiple peaks in the P-V characteristic, (v) dark cloud passing, and (vi) light cloud passing. Finally, the experimental results—through the standard errors and the mean power tracked and tracking factor scores—proved that the proposed hybrid SPF-P&O MPPT technique reaches the convergence to GMPP faster than benchmark approaches when dealing with PSCs.

Development and real time implementation of intelligent holistic power control for stand alone solar photovoltaic generation system

This paper focusses on real-time implementation of an intelligent Holistic Power-Control system which is suitable for uniform, dynamic-irradiance and shaded-conditions for a stand-alone solar-PV system. The model consists of two intelligent Maximum Power Point Tracking(MPPT) control algorithms under different environmental conditions and a novel shading-detection mechanism. A fuzzy logic MPPT system with rule-base developed from real-time solar panel have been implemented. A partial shading detection mechanism based on power loss generated from solar PV array without any sensors is incorporated in the real-time platform. A fuzzy-logic Sweep-Inclusion algorithm is proposed to reach global maximum power point with fast tracking-time under partially-shaded conditions. To test the performance, real-time PV-system with data acquisition using LabVIEW is developed. The standalone solar-PV system is benefitted by the proposed-method by its less tracking-time and more efficiency, accurate partial shading detection through modified power loss scheme and Fuzzy Logic Sweep-Inclusion algorithm for identifying the global-MPPT point.

Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system

This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of a photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation of the duty cycle ensuring optimal power transfer between the PV generator and the load. The neuro-fuzzy hybrid method combines artificial neural network (ANN) to direct the controller to the region where the MPP is located with its reference voltage estimator and its block of neural order. After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. The obtained simulation results using MATLAB/simulink software for the proposed approach compared to ANN and the perturb and observe (P&O), proved that neuro-fuzzy approach fulfilled to extract the optimum power with pertinence, efficiency and precision