Journal articles on the topic 'INTELLIGENT MPPT TECHNIQUES'
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Abidi, Hsen, Lilia Sidhom, and Ines Chihi. "Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques." Energies 16, no. 8 (April 18, 2023): 3509. http://dx.doi.org/10.3390/en16083509.
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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.
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Bhukya, Laxman, Narender Reddy Kedika, and Surender Reddy Salkuti. "Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review." Algorithms 15, no. 10 (September 29, 2022): 365. http://dx.doi.org/10.3390/a15100365.
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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.
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Alhumade, Hesham, Essam H. Houssein, Hegazy Rezk, Iqbal Ahmed Moujdin, and Saad Al-Shahrani. "Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems." Mathematics 11, no. 4 (February 14, 2023): 979. http://dx.doi.org/10.3390/math11040979.
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Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the AHA combined with genetic operators called mAHA. The experimental results show that the proposed mAHA improved the convergence speed and achieved better effective search results. Consequently, the proposed mAHA was used for the first time to find the global maximum power point (MPP). Low efficiency is a drawback of photovoltaic (PV) systems that explicitly use shading. Normally, the PV characteristic curve has an MPP when irradiance is uniform. Therefore, this MPP can be easily achieved with conventional tracking systems. With shadows, however, the conditions are completely different, and the PV characteristic has multiple MPPs (i.e., some local MPPs and a single global MPP). Traditional MPP tracking approaches cannot distinguish between local MPPs and global MPPs, and thus simply get stuck at the local MPP. Consequently, an optimized MPPT with a metaheuristic algorithm is required to determine the global MPP. Most MPPT techniques require more than one sensor, e.g., voltage, current, irradiance, and temperature sensors. This increases the cost of the control system. In the current research, a simple global MPPT method with only one sensor is proposed for PV systems considering the shadow conditions. Two shadow scenarios are considered to evaluate the superiority of the proposed mAHA. The obtained results show the superiority of the proposed single sensor based MPPT method for PV systems.
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Umar, Dallatu Abbas, Gamal Alkawsi, Nur Liyana Mohd Jailani, Mohammad Ahmed Alomari, Yahia Baashar, Ammar Ahmed Alkahtani, Luiz Fernando Capretz, and Sieh Kiong Tiong. "Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems." Processes 11, no. 5 (May 8, 2023): 1420. http://dx.doi.org/10.3390/pr11051420.
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As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.
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Bollipo, Ratnakar Babu, Suresh Mikkili, and Praveen Kumar Bonthagorla. "Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation." IET Renewable Power Generation 14, no. 9 (June 12, 2020): 1433–52. http://dx.doi.org/10.1049/iet-rpg.2019.1163.
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Chaurasia, G. S., Sanjay Agrawal, and N. K. Sharma. "Comparative Analysis of Various MPPT-Techniques for Optimization of Solar-PVEC System." Global Journal of Enterprise Information System 9, no. 3 (September 27, 2017): 94. http://dx.doi.org/10.18311/gjeis/2017/15869.
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<p>The paper aims to provide a comprehensive analysis of various MPPT Technique under various environmental condition. The photovoltaic array having non-linear power voltage characteristic and under non-uniform irradiances. It shows a many peaks which have many local peaks and one global peak. For getting a global peak among all local peaks, MPPT play an important role in PV system. Therefore a technique like maximum power point tracking (MPPT) is required to optimize the performance. Here the comparison of hill climbing perturb and observe (P&O) algorithm technique, incremental and conductance (I&C) control algorithm, the drift free P&O algorithm technique are discussed in detail with simulation and simultaneously some other intelligent control techniques comparison are given briefly which help the researcher to ease in selecting the appropriate algorithm for specific application.</p>
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Guerra, Maria I. S., Fábio M. Ugulino de Araújo, Mahmoud Dhimish, and Romênia G. Vieira. "Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter." Energies 14, no. 22 (November 9, 2021): 7453. http://dx.doi.org/10.3390/en14227453.
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Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.
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Rudraram, Ramesh, Sasi Chinnathambi, and Manikandan Mani. "PV Integrated UPQC with Intelligent Control Techniques for Power Quality Enhancement." International Journal of Electrical and Electronics Research 11, no. 1 (March 30, 2023): 202–12. http://dx.doi.org/10.37391/ijeer.110128.
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The configuration and control of a Unified Power Quality Conditioner (UPQC) coupled with Photovoltaic (PV) system is proposed in this work. By integrating PV to UPQC, the twin advantages of decarbonized clean energy generation in addition to enhanced Power Quality (PQ) is obtained. The series and shunt compensators, which together constitute the UPQC are sequentially interfaced to the common dc-link. In addition to infusing active PV generated power, the UPQC shunt compensator diminishes the load side power quality concerns. The role of a series compensator is to ensure that both the load and source voltages are in-phase perfectly. The PV system is integrated to the UPQC through a DC/DC Interleaved Cuk converter and by regulating the duty cycle of the Interleaved Cuk converter, utmost possible power is derived from the PV using Crow Search Algorithm (CSA) assisted Perturb and Observation (P & O) Maximum Power Point Tracking (MPPT) technique. The designed hybrid MPPT technique is capable of operating at Maximum Power Point (MPP) under both Partial Shading Condition (PSC) and uniform insolation condition. A d-q theory-based control is employed with the assistance of Proportional Integral (PI) controller for controlling the working of UPQC. The dynamic working of the PV based UPQC is evaluated on the basis of simulation outcomes attained from MATLAB.
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Ahmed Hashem, Seham, Raghad Hameed Ahmed, and Suhad Hasan Rhaif. "FUZZY LOGIC CONTROL TO PROCESS CHANGE IRRADIATION AND TEMPERATURE IN THE SOLAR CELL BY CONTROLLING FOR MAXIMUM POWER POINT." Journal of Engineering and Sustainable Development 27, no. 1 (January 1, 2023): 28–36. http://dx.doi.org/10.31272/jeasd.27.1.3.
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This paper presents intelligent control methods to get the maximum power point (MPPT) to be a photovoltaic system that operates with high efficiency when weather conditions change as well as fluctuations in temperatures resulting from sunlight. The proposed method of controlling by fuzzy control techniques is applied with a Direct current to Direct current (DC-DC) converter device. The important steps of the control unit for integrated design. The photovoltaic system, which was designed by Matlab / Simulink, was implemented with simulations of autonomous water pumping techniques. Comparison of results with simulations without MPPT control. We have noticed that the system in the case of using the MPPT that was used in the fuzzy logic unit gave high efficiency for the energy production from the solar cell the crucial control unit steps for integrated design. Simulated autonomous water pumping methods were used to implement the Matlab/Simulink-designed photovoltaic system. Results comparison with simulations lacking MPPT control. We have observed that the system provided great efficiency for the energy generation from the solar cell in the event of using the MPPT that was used in the fuzzy logic unit.
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Iman, M. I., M. F. Roslan, Pin Jern Ker, and M. A. Hannan. "An Intelligent Maximum Power Point Tracking Algorithm for Photovoltaic System." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 457. http://dx.doi.org/10.14419/ijet.v7i4.35.22861.
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This work comprehensively demonstrates the performance analysis of Fuzzy Logic Controller (FLC) with Particle Swarm Optimization (PSO) Maximum Power Point Tracker (MPPT) algorithm on a stand-alone Photovoltaic (PV) applications systems. A PV panel, DC-DC Boost converter and resistive load was utilized as PV system. Three different MPPT algorithms were implemented in the converter. The result obtained from the converter was analyzed and compared to find the best algorithm to be used to identify the point in which maximum power can be achieve in a PV system. The objective is to reduce the time taken for the tracking of maximum power point of PV application system and minimize output power oscillation. The simulation was done by using MATLAB/Simulink with DC-DC Boost converter. The result shows that FLC method with PSO has achieved the fastest response time to track MPP and provide minimum oscillation compared to conventional P&O and FLC techniques.
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Dr. C. Kathirvel, R. Mohan Kumar,. "Research Survey on Different MPP Tracking Optimization Algorithms for Solar Photovoltaic System (SPS)." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 2666–71. http://dx.doi.org/10.17762/turcomat.v12i6.5767.
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Due to increase in global warming, it is required to choose an alternative renewable energy source for the electricity generation. Among various renewable energy sources (RES), photo-voltaic energy is one of the most accessible source of energies. But the conversion rate of solar PV cell is about 25 % to 40 % of solar irradiation level. In Solar Photovoltaic (PV) system, to improve and maximize the operating efficiency level, Maximum Power Point Tracking (MPPT) techniques were required. Because of the change in the level of solar irradiance, and the nature of dynamic temperature, this MPP tracking will be highly important to make the solar PV system (SPS) to operate at higher efficiency level. This MPPT method is mainly categorized into three different types such as direct method, indirect method and intelligent method. This paper will gives and overview about various MPPT methods employed for solar PV system. Various controlling algorithms were discussed in this section for a better understanding.
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Pathy, Subramani, Sridhar, Thamizh Thentral, and Padmanaban. "Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study." Energies 12, no. 8 (April 16, 2019): 1451. http://dx.doi.org/10.3390/en12081451.
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PV generating sources are one of the most promising power generation systems in today’s power scenario. The inherent potential barrier that PV possesses with respect to irradiation and temperature is its nonlinear power output characteristics. An intelligent power tracking scheme, e.g., maximum power point tracking (MPPT), is mandatorily employed to increase the power delivery of a PV system. The MPPT schemes experiences severe setbacks when the PV is even shaded partially as PV exhibits multiple power peaks. Therefore, the search mechanism gets deceived and gets stuck with the local maxima. Hence, a rational search mechanism should be developed, which will find the global maxima for a partially shaded PV. The conventional techniques like fractional open circuit voltage (FOCV), hill climbing (HC) method, perturb and observe (P&O), etc., even in their modified versions, are not competent enough to track the global MPP (GMPP). Nature-inspired and bio-inspired MPPT techniques have been proposed by the researchers to optimize the power output of a PV system during partially shaded conditions (PSCs). This paper reviews, compares, and analyzes them. This article renders firsthand information to those in the field of research, who seek interest in the performance enhancement of PV system during inhomogeneous irradiation. Each algorithm has its own advantages and disadvantages in terms of convergence speed, coding complexity, hardware compatibility, stability, etc. Overall, the authors have presented the logic of each global search MPPT algorithms and its comparisons, and also have reviewed the performance enhancement of these techniques when these algorithms are hybridized.
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Rkik, Iliass, Mohamed El khayat, Hafsa Hamidane, Abdelali Ed-Dahhak, Mohammed Guerbaoui, and Abdeslam Lachhab. "An hybrid control strategy design for Photovoltaic battery charger." E3S Web of Conferences 336 (2022): 00067. http://dx.doi.org/10.1051/e3sconf/202233600067.
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This work presents the design and the modelling of an improved lead acid Battery charger for solar photovoltaic applications. In this context, the control unit of the battery charger is composed of two intelligent controllers. In the first state, an MPPT controller based on an Adaptive neuro-fuzzy inference system (ANFIS) is used to extract the full maximum power provided by the PV array, in the second stage, the control unit switches to the regulator mode on the basis of a fuzzy logic control block that offers the three charging stages according to DIN 41773 standard for lead-acid battery. In order to demonstrate the performance of the ANFIS controller, this paper presents also a comparison of several MPPT techniques for solar PV applications.
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Et. al., Yashwant Joshi,. "Intelligent Control Strategy to Enhance Power Smoothing of Renewable based Microgrid with Hybrid Energy Storage." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 8 (May 15, 2021): 3090–100. http://dx.doi.org/10.17762/turcomat.v12i8.4148.
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A stand-alone renewable based microgrid (MG) performance with a hybrid energy storage system has been examined in this work. Stand-alone MG system mainly consists of a solar photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind system. The hybrid energy storage system is based on Ni-Metal- Hydride (NiMH) battery and a supercapacitor (SC). The paper's primary goal is to propose an artificial neural network (ANN) based control strategy for charging/discharging control of Ni-Metal- Hydride battery & supercapacitor. The proposed maximum power tracking techniques (MPPT) include perturb and observe (P& O) algorithm for solar PV system while optimum torque (OT) MPPT for PMSG based wind turbine. The ANN-based control mechanism can maintain the DC bus voltage constant and trigger the supercapacitor to limit the battery current when the battery charging/ discharging current reached its threshold value. The proposed model responds quickly to intermittent nature PV-wind power generation or load power variation.
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Muthu, Annamalai. "Comparative analysis of fuzzy and ANFIS based MPPT controller for wind power generation system." International Journal of Applied Power Engineering (IJAPE) 10, no. 4 (December 1, 2021): 355. http://dx.doi.org/10.11591/ijape.v10.i4.pp355-363.
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<span lang="EN-US">In recent years, huge developments in wind energy production and meet consumer demand. Numerous researchers have focused on maximum energy generation techniques for the wind system. The main reason for this work is to compare the different smart controllers for the maximum power generation techniques in the wind system. In this article, we developed and modeled a 250-watt wind power system in a MATLAB environment and simulated it in different weather conditions. Based on the simulation results, two intelligent controllers, such as fuzzy and ANFIS, were proposed and compared to obtain the maximum energy generation techniques in the wind system. Finally, the optimal smart controller was chosen based on performance.</span>
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Jusoh, M. A., M. F. N. Tajuddin, and M. A. Roslan. "MPP Tracking with a Modified Duty Cycle Sweeping (MDCS) Algorithm for Various Environmental Irradiance Conditions." Journal of Physics: Conference Series 2550, no. 1 (August 1, 2023): 012007. http://dx.doi.org/10.1088/1742-6596/2550/1/012007.
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Abstract In this paper, a photovoltaic (PV) system based on modified duty cycle sweeping (MDCS) has been proposed to achieve the maximum power point tracking (MPPT). The disadvantages of perturb and observe (P&O), such as diverging tracking directions and the inability to detect the global peak during partial shading (PS), are intended to be overcome by this method (PS). An intelligent double identification and tracking method consistently tracks the global peak under partial shading and the MPP under rapid irradiance fluctuations. Strict dynamic irradiance and partial shading tests are imposed in MATLAB/Simulink@ and simulated to validate the suggested concept. Additionally, a laboratory prototype MPPT standalone PV system supported by Texas Instruments’ Code Composer Studio is operated by a SEPIC converter in conjunction with the C2000 real-time microcontroller in order to conduct an experimental validation study. The effectiveness of the method is compared with the other well-known MPPT techniques, conventional P&O. The suggested method successfully follows the global peak under various patterns of partial shading as compared to the conventional algorithms. The algorithm’s efficiency has been preserved at around 95-100%.
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Sahu, Jayanta Kumar, Babita Panda, and Jyoti Prasad Patra. "Artificial neural network for maximum power point tracking used in solar photovoltaic system." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (September 1, 2023): 1694. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1694-1701.
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<span lang="EN-US">Nowadays, non-conventional energy sources like solar, wind, geothermal, and small hydro play a vital role in generating electricity. Among these, solar energy is utilized in urban and rural areas. When the sunlight falls on the solar plate, the PV cell produces charge carriers that produce an electric current. A photo voltaic cell is used when it works at the maximum power point. Traditional maximum power point tracking (MPPT) techniques are easier to structure and apply but perform worse than AI-based systems. The main objective of this paper is to develop an intelligent system to determine the maximum power point using artificial neural networks. This system uses the radial basis function network (RBFN) architecture to improve MPPT control for PV systems. The response characteristics of the photo-voltaic array are non-linear due to insolation, temperature variation, the incident light angle, and the solar cell's surface condition. Hence, this must be checked to develop the system's most significant amount of power. The MPPT controller's response can be recycled to monitor the DC-DC boost converters for maximum efficiency.</span>
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Ul-Haq, Azhar, Shah Fahad, Saba Gul, and Rui Bo. "Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults." Energies 16, no. 2 (January 15, 2023): 974. http://dx.doi.org/10.3390/en16020974.
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Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types.
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Safarishaal, Masoud, and Mohammad Sarvi. "New hybrid maximum power point tracking methods for fuel cell using artificial intelligent." AIP Advances 13, no. 4 (April 1, 2023): 045207. http://dx.doi.org/10.1063/5.0144806.
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An efficient way to raise the proton exchange membrane fuel cell’s (PEMFC’s) power generation efficiency and power supply quality is to use maximum power point tracking (MPPT). Conventional MPPT approaches often have difficulty producing an effective control effect due to the PEMFC’s inherent nonlinear characteristics. Another challenge for systems that track maximum power points is dealing with fast changes in operational conditions that affect FC’s maximum power point (MPP). The main contribution of this study is the introduction of two artificial intelligence-based MPP control approaches for fuel cells operating under rapidly changing operating conditions. These methods are based on imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems (ANFIS) (ICA NN). The proposed approaches determine the fuel cell voltage that corresponds to the maximum power point. Following that, a fuzzy logic controller is used to modify the duty cycle of a DC/DC boost converter for FC MPP tracking. The MATLAB environment is used to run simulations. The results of the proposed method are compared with those of the conventional fuzzy method. The results demonstrate that the suggested solutions function excellently in both normal operating conditions and quickly varying operating conditions. On the other hand, the suggested approaches can quickly locate and monitor the MPP of FC. Additionally, the suggested techniques increase the FC system’s efficiency by absorbing more power.
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Slimi, Mohammed, Abdelkrim Boucheta, and Bousmaha Bouchiba. "Maximum power control for photovoltaic system using intelligent strategies." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 1 (March 1, 2019): 423. http://dx.doi.org/10.11591/ijpeds.v10.i1.pp423-432.
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<p>The power supplied by photovoltaic DC–DC converter is affected by two factors, sun irradiance and temperature. Therefore, to improve the performance of the PV system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique present some difficulties in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). In this paper, we present a comparative study of the PV standalone system which is controlled by three techniques. The first one is conventional based on the observation and perturbation technique, the other are intelligent based on fuzzy logic according Mamdani and Takagi-Sugeno models. The investigations show that the fuzzy logic controllers provide the best results and Takagi-Sugeno model presents the lower overshoot value.</p>
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Abboud, Sarah, Rachid Habachi, Abdellah Boulal, and Semma El Alami. "Maximum power point tracker using an intelligent sliding mode controller of a photovoltaic system." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 1 (March 1, 2023): 516. http://dx.doi.org/10.11591/ijpeds.v14.i1.pp516-524.
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<span lang="EN-US">The operating performance of a PV module/array is extremely reliant on the weather (temperature/irradiation) and non-linear. Thus, to ensure that the PV array produces the maximum possible power at any time and regardless of the external conditions, maximum power point tracking (MPPT) techniques are required. The solution suggested in this paper involves taking into account two cascaded controllers as follows; the incremental conductance (INC) controller, which is intended to provide a reference proportional to the PV array's optimal power P<sub>MPP</sub>, and the sliding mode control (SMC), which is in charge of controlling the GPV voltage. The strategy of the SMC is to design a sliding surface that defines the operating point. The SMC combined with the INC aims to achieve fast MPPT action on PV systems using cascade control. The proposed controller is robust to changing weather conditions. In order to evaluate what is done, the results are compared with the INC+PI controller. When an abrupt change occurs, the SMC has a low transient and arrives to equilibrium sooner than the INC+PI controller. the results are presented by the PSIM software, and demonstrate the SMC controller's performance while confirming that the new approach has increased both production and energy efficiency.</span>
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Pamuk, Nihat. "Performance Analysis of Different Optimization Algorithms for MPPT Control Techniques under Complex Partial Shading Conditions in PV Systems." Energies 16, no. 8 (April 11, 2023): 3358. http://dx.doi.org/10.3390/en16083358.
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Classic algorithms show high performance in tracking the maximum power point (MPP) of photovoltaic (PV) panels under uniform irradiance and temperature conditions. However, when partial or complex partial shading conditions occur, they fail in capturing the global maximum power point (GMPP) and are trapped in one of the local maximum power points (LMPPs) leading to a loss in power. On the other hand, intelligent algorithms inspired by nature show successful performance in GMPP tracking. In this study, an MPPT system was set up in MATLAB/Simulink software consisting of six groups of serially connected PV panels, a DC-DC boost converter, and load. Using this system, the cuckoo search (CS) algorithm, the modified incremental conductivity (MIC) algorithm, the particle swarm optimization (PSO) algorithm, and the grey wolf optimization (GWO) algorithm were compared in terms of productivity, convergence speed, efficiency, and oscillation under complex shading conditions. The results showed that the GWO algorithm showed superior performance compared to the other algorithms under complex shading conditions. It was observed that GWO did not oscillate during GMPP tracking with an average convergence speed of 0.22 s and a tracking efficiency of 99%. All these evaluations show that GWO is a very fast, highly accurate, efficient, and stable MPPT method under complex partial shading conditions.
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Abderrahim, Zemmit, Herraguemi Kamel Eddine, and Messalti Sabir. "A New Improved Variable Step Size MPPT Method for Photovoltaic Systems Using Grey Wolf and Whale Optimization Technique Based PID Controller." Journal Européen des Systèmes Automatisés 54, no. 1 (February 28, 2021): 175–85. http://dx.doi.org/10.18280/jesa.540120.
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In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.
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Katche, Musong L., Augustine B. Makokha, Siagi O. Zachary, and Muyiwa S. Adaramola. "A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems." Energies 16, no. 5 (February 24, 2023): 2206. http://dx.doi.org/10.3390/en16052206.
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Renewable Energy technologies are becoming suitable options for fast and reliable universal electricity access for all. Solar photovoltaic, being one of the RE technologies, produces variable output power (due to variations in solar radiation, cell, and ambient temperatures), and the modules used have low conversion efficiency. Therefore, maximum power point trackers are needed to harvest more power from the sun and to improve the efficiency of photovoltaic systems. This paper reviews the methods used for maximum power point tracking in photovoltaic systems. These methods have been classified into conventional, intelligent, optimization, and hybrid techniques. A comparison has also been made of the different methods based on criteria such as tracking speed, efficiency, cost, stability, and complexity of implementation. From the literature, it is clear that hybrid techniques are highly efficient compared to conventional methods but are more complex in design and more expensive than the conventional methods. This review makes available useful information that can be exploited when choosing or designing MPPT controllers.
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Mao, Mingxuan, Lichuang Cui, Qianjin Zhang, Ke Guo, Lin Zhou, and Han Huang. "Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies." Energy Reports 6 (November 2020): 1312–27. http://dx.doi.org/10.1016/j.egyr.2020.05.013.
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González-Castaño, Catalina, Carlos Restrepo, Javier Revelo-Fuelagán, Leandro L. Lorente-Leyva, and Diego H. Peluffo-Ordóñez. "A Fast-Tracking Hybrid MPPT Based on Surface-Based Polynomial Fitting and P&O Methods for Solar PV under Partial Shaded Conditions." Mathematics 9, no. 21 (October 28, 2021): 2732. http://dx.doi.org/10.3390/math9212732.
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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.
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Shenoy, K. Latha, C. Gurudas Nayak, and Rajashekar P. Mandi. "Effect of partial shading in grid connected solar PV system with FL controller." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 1 (March 1, 2021): 431. http://dx.doi.org/10.11591/ijpeds.v12.i1.pp431-440.
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As conventional fossil fuel reserves shrink and the danger of climate change prevailing, the need for alternative energy sources is unparalleled. A smart approach to compensate the dependence on electricity generated by burning fossil fuels is through the power generation using grid connected PV system. Partial shading on PV array affects the quantity of the output power in photovoltaic (PV) systems. To extract maximum power from PV under variable irradiance, variable temperature and partial shading condition, various MPPT algorithms are used. Incremental conductance and fuzzy based MPPT techniques are used for maximum power extraction from PV array. Basically 11 kW Solar PV system comprising of PV array coupled with an Inverter through a dc-dc converter is considered for the analysis and output of the inverter is supplied to the load through the LCL filter. An Intelligent controller for maximum power point tracking of PV power is designed. Also, a fuzzy controller for VSC is developed to improve the system performance. The above proposed design has been simulated in the MATLAB/Simulink and analyzed the system performance under various operating conditions. Finally, the performance is evaluated with IEEE 1547 standard for showing the effectiveness of the system.
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Anand, R., and Dr S. Saravanan. "Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive." Circuits and Systems 07, no. 08 (2016): 1635–52. http://dx.doi.org/10.4236/cs.2016.78142.
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Sarwar, Sajid, Muhammad Annas Hafeez, Muhammad Yaqoob Javed, Aamer Bilal Asghar, and Krzysztof Ejsmont. "A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions." Energies 15, no. 5 (March 3, 2022): 1880. http://dx.doi.org/10.3390/en15051880.
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The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.
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Syafaruddin, Syafaruddin. "Review of Maximum Power Point Tracking Control of Photovoltaic Systems in Case of Uniform & Non-uniform Irradiance Conditions." Proceeding International Conference on Science and Engineering 1 (October 31, 2017): xv. http://dx.doi.org/10.14421/icse.v1.319.
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It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. One of the approaches to increase the efficiency of PV power generation system is to operate the PV systems optimally at the maximum power point. However, the PV system can be optimally operated only at a specific output voltage; otherwise the output power fluctuates under intermittent weather conditions. In addition, it is very difficult to test the performance of PV systems controller under the same weather condition during the development process where the field testing is costly and time consuming. For these reasons, the presentation is about the state of the art techniques to track the maximum available output power of photovoltaic systems called maximum power point tracking (MPPT) control systems. This topic could be also one of the most challenges in photovoltaic systems application that has been receiving much more attention worldwide. The talks will cover the application of intelligent techniques by means the artificial neural network (ANN) and fuzzy logic controller scheme using polar information to develop a novel real-time simulation technique for MPPT control by using dSPACE real-time interface system. In this case, the three-layer feed-forward ANN is trained once for different scenarios to determine the global MPP voltage and power and the fuzzy logic with polar information controller takes the global maximum power point (MPP) voltage as a reference voltage to generate the required control signal for the power converter. This type of fuzzy logic rules is implemented for the first time in MPPT control application. The proposed method has been tested using different solar cell technologies such as monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies. In other cases, one of the main causes of reducing energy yield of photovoltaic systems is the partially shaded condition. Although the conventional MPPT control algorithms operate well in a uniform solar irradiance, they do not operate well in non-uniform solar irradiance conditions. The non-uniform conditions cause multiple local maximum power points on the power-voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global power point may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognize the global operating point under partially shaded conditions. From these reasons, the presentation will address the effectiveness of the proposed MPPT method to solve the partially shaded conditions under the experimental real-time simulation technique based dSPACE real-time interface system for different size of PV arrays, such as 3x3(0.5kW) and 20x3(3.3kW) and different interconnected PV arrays, for instance series-parallel (SP), bridge link (BL) and total cross tied (TCT) configurations.
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Ibnelouad, Aouatif, Abdeljalil Elkari, Hassan Ayad, and Mostafa Mjahed. "A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 1252. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1252-1264.
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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
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Samara, Sufyan, and Emad Natsheh. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems." Sustainability 12, no. 5 (March 5, 2020): 2011. http://dx.doi.org/10.3390/su12052011.
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The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.
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L, Anbarasu, Gopinath S, Jaikavin K, and Selvasundaram D. "Intelligent Controller for Grid Connected by Charging Station towards Achieving Sustainable Development." Journal of Controller and Converters 7, no. 3 (November 18, 2022): 4–8. http://dx.doi.org/10.46610/jcc.2022.v07i03.002.
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This study suggests power management techniques for a PV storage system that is connected to the grid in an electric vehicle charging station (EVCS). The CS's power control system is where the strategy is intended to be used. To lower energy consumption costs calculated using the electrical grid in stand-alone mode and to minimize stress on existing power, the control relies upon relating to the use of renewable energy sources using an optimization process. The outcomes of a 15kW PV-Grid system's simulation coupled with a load flow of five EVs and a 40 kWh lithium-ion battery are used in this paper to describe the approach in detail. However, this study describes a powerful predictive model that is founded on real-time power monitoring supply and demand. When an effective data connection is made between the CS and the plugged EV. The method to determine the best charging mode also takes into account several other factors, such as the PV array's instantaneous power, the amount of energy in the buffer for battery storage, and the restricted power available from the grid. The source converters for voltage the MPPT algorithm, additionally, the ongoing control loop serves as the foundation for the power forecasting model that has been chosen. The outcomes of the simulation of various scenarios for charging effectively describe the efficiency of the suggested CS, which is used to test the validity of this model.
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Masry, Mohamed Zaghloul-El, Abdallah Mohammed, Fathy Amer, and Roaa Mubarak. "New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems." Sustainability 15, no. 14 (July 11, 2023): 10884. http://dx.doi.org/10.3390/su151410884.
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This research aimed to increase the power captured from photovoltaic (PV) systems by continuously adjusting the PV systems to work at the maximum power point under climate changes such as solar irradiance change and temperature change and by tracking the global maximum power under partial shading conditions (PSCs). Under the effect of partial shading (PS), the PV curve has many local maximum peaks (LMPs) and one global maximum peak (GMP) which is dynamic because it changes with time when the shading pattern (SP) changes. The traditional maximum power point tracking (MPPT) methods are unable to track the Dynamic GMP and may fall into one of the LMPs. Many modern MPPT methods have been introduced that can track the Dynamic GMP, but their effectiveness can be improved. In this respect, this work introduces a new optimal MPPT technique to enhance the performance of the maximum power point tracking of solar cells under environmental changes and partial shading conditions. The proposed technique combines three well-known and important MPPT techniques, which are the Artificial Neural Network (ANN), Variable Step Perturb and Observe (VSP&O), and Fuzzy Logic Controller (FLC). Artificial Neural Network gives a voltage near the optimum voltage, Variable Step Perturb and Observe updates the voltage to get close to the optimum voltage, and Fuzzy Logic Controller updates the step size of the (P&O) technique. The proposed hybrid ANN-VSP&O-FLC technique showed its ability to track the Dynamic GMP accurately and quickly under the variation in the shading patterns with time and its ability to follow maximum power efficiently and quickly under climate changes. The proposed hybrid ANN-VSP&O-FLC technique also showed very low distortions in waveforms and very low oscillations around the steady state. The proposed hybrid ANN-VSP&O-FLC technique was compared to the most recent and effective MPPT techniques in terms of steady-state behavior, tracking speed, tracking efficiency, and distortions in waveforms, and the comparison showed that it is superior to them, with lower distortions in waveforms, a faster tracking speed (less than 0.1 s), higher tracking efficiency (greater than 99.65%), and lower oscillations around the steady state (less than 2 Watts).
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Ali, Amjad, Kashif Irshad, Mohammad Farhan Khan, Md Moinul Hossain, Ibrahim N. A. Al-Duais, and Muhammad Zeeshan Malik. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review." Sustainability 13, no. 19 (September 23, 2021): 10575. http://dx.doi.org/10.3390/su131910575.
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Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.
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Krachai, Saidia Della, A. Boudghene Stambouli, M. Della Krachai, and M. Bekhti. "Experimental investigation of artificial intelligence applied in MPPT techniques." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 4 (December 1, 2019): 2138. http://dx.doi.org/10.11591/ijpeds.v10.i4.pp2138-2147.
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Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. <br />In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality. <br /><br />
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Agdam, Mohammed, Abdallah Asbayou, Mustapha Elyaqouti, Ahmed Ihlal, and Khaled Assalaou. "MPPT of PV System Under Partial Shading Conditions Based on Bio-inspired Swarm Intelligence Technique." E3S Web of Conferences 297 (2021): 01051. http://dx.doi.org/10.1051/e3sconf/202129701051.
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To respond to the increase in demand for electricity, the use of photovoltaics is growing considerably as it produces electrical energy without polluting the environment. In addition, to enhance the efficiency of photovoltaic modules, an MPPT algorithm is required to follow the maximum voltage and maximum current in the IV curve. This technique can be achieved by using a DC-DC converter. For this purpose, various MPPT techniques have been developed. The combination of MPPT and DC-DC converter is implemented using Matlab/Simulink and connected to a modelled PV module to validate the simulation.
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Abutaima, Khaleel Abed, and Ramizi Mohamed. "Design of Improved Incremental Conductance with Fast Intelligent (FI) Based MPPT Technique for Solar PV System." Jurnal Kejuruteraan 34, no. 6 (November 30, 2022): 1093–104. http://dx.doi.org/10.17576/jkukm-2022-34(6)-10.
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Currently, the solar PV power extraction technology is undergoing significant improvement. Towards this, the paper proposed the design for a photovoltaic (PV) array and the output performance of a photovoltaic system under the influence of irradiance. To achieve this, the design for improved incremental and conductance fast tracking INC -FI based MPPT technique for solar PV system has been presented. The purpose of employing the improved INC -FI technique is to improve the efficiency of the system. The accuracy and performance of the proposed INC -FI method was increased due to its better tracking capability by utilizing variable ΔD for tracking the MPP in comparison to the conventional INC method at variable temperature while keeping the irradiance constant. Further, the results of the proposed method were compared with the conventional method where the INC -FI based technique outperforms the conventional INC method in terms of better accuracy. For the irradiance with 800w/m2, the achieved MPPT efficiency was 58.21 for conventional method and 80.53 for the improved technique. It was also noted that the tracking efficiency of the conventional method was 84.39 as compared to 99.92 for the proposed INC -FI technique in terms of MPPT efficiency at the irradiance of 1000w/m2. Furthermore, the improved method delivered fast tracking ability of the MPPT system with a time of less than 10 s(approx.). The MATLAB Simulink platform was utilized for designing the proposed technique. In future, the proposed INC based technique would be implemented on hardware for better outcomes and validation.
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Khan, Mohammad Junaid, Divesh Kumar, Yogendra Narayan, Hasmat Malik, Fausto Pedro García Márquez, and Carlos Quiterio Gómez Muñoz. "A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks." Energies 15, no. 9 (May 4, 2022): 3352. http://dx.doi.org/10.3390/en15093352.
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The development of each country depends on electricity. In this regard, conventional energy sources, e.g., diesel, petrol, etc., are decaying. Consequently, the investigations of renewable energy sources (RES) are increasing as alternate energy sources for the fulfillment of energy requirements. The output characteristics of RES are becoming non-linear. Therefore, the Maximum Power Point Tracking (MPPT) techniques are critical for extracting the Maximum Power Point (MPP) from RES, e.g., photovoltaic (PV) and wind turbines (WT). RES such as the Fuel Cell (FC) has been hailed as one of the major capable RES for automobile applications since they continually create electricity for the dc-link (even if one or both RES are not supplied by solar and wind, the FC will continue to supply to the load). Adaptive Neuro-Fuzzy Inference System (AN-FIS) MPPT for PV, WT, FC, and Hybrid RES is employed in this research article to solve this problem. The high step-ups (boost converters) are connected with PV and FC modules, and the buck converter is connected with the WT framework, to extract the maximum amount of power using MPPT algorithms. The performance of proposed frameworks based on MPPT algorithms is assessed in variable operating conditions such as Solar-Radiation (SR), Wind-Speed (WS), and Hydrogen-Fuel-Rate (HFR). A novel AN-FIS MPPT framework has enhanced the power of Hybrid RES at DC-link, and also reduced the simulation time to reach the MPP when compared to the perturb and observe (P-&-O), Fuzzy-Logic Controller (F-LC), and artificial neural network (AN-N) MPPTs.
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Bouakkaz, Mohammed Salah, Ahcene Boukadoum, Omar Boudebbouz, Issam Attoui, Nadir Boutasseta, and Ahmed Bouraiou. "Survey of Six Maximum Power Point Tracking Algorithms under Standard Test conditions." Algerian Journal of Renewable Energy and Sustainable Development 03, no. 01 (June 15, 2021): 53–62. http://dx.doi.org/10.46657/ajresd.2021.3.1.6.
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In this work, a survey is carried out on six MPPT algorithms which include conventional and artificial intelligence based approaches. Maximum Power Point Tracking (MPPT) algorithms are used in PV systems to extract the maximum power in varying climatic conditions. The following most popular MPPT techniques are being reviewed and studied: Hill Climbing (HC), Perturb and Observe (P&O), Incremental Conductance (INC), Open-Circuit Voltage (OCV), Short Circuit Current (SCC), and Fuzzy Logic Control (FLC). The algorithms are evaluated, analyzed, and interpreted using a Matlab-Simulink environment to show the performance and limitations of each algorithm
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Zhou, C., Z. Y. Liu, Y. N. Sun, and L. Mao. "A novel maximum power point tracking technique with improved particle swarm optimization for proton exchange membrane fuel cell." Journal of Physics: Conference Series 2347, no. 1 (September 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2347/1/012017.
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Abstract 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.
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Yung Yap, Kah, Charles R. Sarimuthu, and Joanne Mun-Yee Lim. "Artificial Intelligence Based MPPT Techniques for Solar Power System: A review." Journal of Modern Power Systems and Clean Energy 8, no. 6 (2020): 1043–59. http://dx.doi.org/10.35833/mpce.2020.000159.
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Balal, Afshin, Mostafa Abedi, and Farzad Shahabi. "Optimized generated power of a solar PV system using an intelligent tracking technique." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (December 1, 2021): 2580. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2580-2592.
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<span lang="EN-US">Partial shading condition (PSC) is common and complicated in all types of PV power plant. Therefore, the power production of solar system would be affected by the mismatch phenomena produced by PSC. Furthermore, when the array is partially shaded, the P–V characteristics become more complex which causes multiple peaks of the P-V curve. So, the simple maximum power point tracking (MPPT) methods such as perturb and observe (P&O) will fail. To address the above issue, this paper proposes a combination of two different approaches, implementing distributed MPPT (DMPPT) and optimized fuzzy/bee algorithm (OFBA). DMPPT can be utilized to maximize solar energy by allowing each module, or group of modules, be managed independently. Also, due to the output power oscillations around the operating point in the P&O method, an OFBA is employed which allowing for the decrease of output power oscillations without the usage of temperature and light sensors. The result shows that utilizing of DMPPT control approach in conjunction with the OFBA boosts the output generated power.</span>
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Hichem, Louki, Omeiri Amar, and Merabet Leila. "Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 1960–81. http://dx.doi.org/10.11591/beei.v12i4.5099.
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Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.
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Hichem, Louki, Omeiri Amar, and Merabet Leila. "Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 1960–81. http://dx.doi.org/10.11591/eei.v12i4.5099.
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Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.
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Abo-Sennah, M. A., M. A. El-Dabah, and Ahmed El-Biomey Mansour. "Maximum power point tracking techniques for photovoltaic systems: a comparative study." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 57. http://dx.doi.org/10.11591/ijece.v11i1.pp57-73.
Full textAbstract:
Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
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Jarmouni, Ezzitouni, Ahmed Mouhsen, Mohamed Lamhamedi, Hicham Ouldzira, and Ilias En-naoui. "Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (October 7, 2022): 1276. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1276-1285.
Full textAbstract:
Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.
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Diouri, Omar, Ahmed Gaga, Saloua Senhaji, and Mohammed Ouazzani Jamil. "Design and PIL Test of High Performance MPPT Controller Based on P&O-Backstepping Applied to DC-DC Converter." Journal of Robotics and Control (JRC) 3, no. 4 (July 1, 2022): 431–38. http://dx.doi.org/10.18196/jrc.v3i4.15184.
Full textAbstract:
This paper presents the design, test and validation process of the maximum power point tracking (MPPT) based on the Perturb and Observe backstepping controller. The design of this robust controller follows a sequence of two tests of the validated model-based design (MBD) approach. Our contribution is to give a roadmap for designing, testing and validating embedded software for MPPT algorithms. Perturb and observe algorithm is used to generate the reference voltage which is used by the backstepping controller to generate the maximum power. Then, after simulation of all these techniques, generated optimized C code for the STM32F4 microcontroller is necessary to test the controller on embedded platform. Therefore, the algorithm of MPPT is simulated by Model in the Loop (MIL) and Processor in the Loop (PIL) techniques. The results show that the proposed system has full control over reference power, for different atmospheric changes, by backstepping and integrating into a 32-bit ARM microcontroller. In all of the various tests, the embedded software developed demonstrates high compliance and high performance with MPPT requirements.
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Murtaza, Ali Faisal, Hadeed Ahmed Sher, Filippo Spertino, Alessandro Ciocia, Abdullah M. Noman, Abdullrahman A. Al-Shamma’a, and Abdulaziz Alkuhayli. "A Novel MPPT Technique Based on Mutual Coordination between Two PV Modules/Arrays." Energies 14, no. 21 (October 25, 2021): 6996. http://dx.doi.org/10.3390/en14216996.
Full textAbstract:
A novel maximum power point tracking (MPPT) technique based on mutual coordination of two photovoltaic (PV) modules/arrays has been proposed for distributed PV (DPV) systems. The proposed technique works in two stages. Under non-mismatch conditions between PV modules/arrays, superior performance stage 1 is active, which rectifies the issues inherited by the perturb and observe (P&O) MPPT. In this stage, the technique revolves around the perturb and observe (P&O) algorithm containing an intelligent mechanism of leader and follower between two arrays. In shading conditions, stage 2 is on, and it works like conventional P&O. Graphical analysis of the proposed technique has been presented under different weather conditions. Simulations of different algorithms have been performed in Matlab/Simulink. Simulation results of the proposed technique compliment the graphical analysis and show a superior performance and a fast response as compared to others, thus increasing the efficiency of distributed PV systems.
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Ali, Mahmoud N., Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish. "Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic." Sensors 21, no. 4 (February 10, 2021): 1244. http://dx.doi.org/10.3390/s21041244.
Full textAbstract:
This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.