Готові списки джерел за темами / INTELLIGENT MPPT TECHNIQUES

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Добірка наукової літератури з теми "INTELLIGENT MPPT TECHNIQUES"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "INTELLIGENT MPPT TECHNIQUES"

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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Анотація:

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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Більше джерел

Дисертації з теми "INTELLIGENT MPPT TECHNIQUES"

KAUSHIK, CHIRAG. "COMPARITIVE STUDY OF TRADITIONAL AND INTELLIGENT MPPT TECHNIQUES FOR PHOTOVOLTAIC SYSTEMS." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19258.

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Electricity has become a basic necessity for humans in day-to-day life to fuel all kinds of industries, agriculture, households etc. In India, major portion of electricity is generated using fossil fuels like coal, oil and natural gas. As the fossil fuels are depleting at a fast pace, there is an urgent need of switching to renewable resources for power generation. Solar energy is seen as one of the most promising renewable energy source. Presently, in India 13.5% of electricity is generated from solar energy whereas 59.1% of it is generated from fossil fuels[1]. A lot of research is going on related to Solar Photovoltaic(SPV) systems to enhance its efficiency with higher affordability which can make it much more feasible and sustainable for human needs. SPV converts the solar energy from sunlight to electrical energy using photovoltaic effect. The DC power is efficiently extracted by the solar PV array using the optimum firing of the MOSFET/IGBT switch of the converter using Maximum Power Point Tracking (MPPT) techniques. The various MPPT techniques being compared in the project are ‘Perturb & Observe Algorithm’, ‘Incremental Conductance Algorithm’, ‘Type-1 Fuzzy Logic Controller’ and Artificial neural Network. The photovoltaic array of 13.68kW of SunPower SPR-76R-BLK-U module with 45 series connected modules in 4 parallel strings is used for the present studies. The MATLAB Simulink Model for standalone SPV system using boost converter is developed and validated under uniformly varying atmospheric conditions. Comparative analysis between MPPT techniques has been carried out based on fill factor, power loss, power extracted and ripples.

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Частини книг з теми "INTELLIGENT MPPT TECHNIQUES"

Tiwari, Neeraj, Amit Saraswat, Ravi Soni, and Suchi Rawat. "Performance Analysis of Fabricated Buck-Boost MPPT Charge Controller." In Intelligent Computing Techniques for Smart Energy Systems, 761–68. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_79.

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Singh, Bhuwan Pratap, Sunil Kumar Goyal, and Shahbaz Ahmed Siddiqui. "Analysis and Classification of Maximum Power Point Tracking (MPPT) Techniques: A Review." In Intelligent Computing Techniques for Smart Energy Systems, 999–1008. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_106.

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Mitiku, Tigilu, and Mukhdeep Singh Manshahia. "A Literature Review on the MPPT Techniques Applied in Wind Energy Harvesting System." In Intelligent Computing & Optimization, 762–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93247-3_73.

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Sahu, Pankaj, and Rajiv Dey. "A Comparative Analysis of IC and RCC MPPT Techniques for High-Power PV Systems." In Algorithms for Intelligent Systems, 317–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2109-3_31.

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Hussaian Basha, CH, C. Rani, R. M. Brisilla, and S. Odofin. "Simulation of Metaheuristic Intelligence MPPT Techniques for Solar PV Under Partial Shading Condition." In Advances in Intelligent Systems and Computing, 773–85. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0035-0_63.

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Behera, S., D. Meher, and S. Poddar. "Comparative Study of MPPT Control of Grid-Tied PV Generation by Intelligent Techniques." In Advances in Intelligent Systems and Computing, 213–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8055-5_20.

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Jately, Vibhu, and Sudha Arora. "Performance Investigation of Hill-Climbing MPPT Techniques for PV Systems Under Rapidly Changing Environment." In Advances in Intelligent Systems and Computing, 1145–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_120.

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Farayola, Adedayo M., Ali N. Hasan, Ahmed Ali, and Bhekisipho Twala. "Distributive MPPT Approach Using ANFIS and Perturb&Observe Techniques Under Uniform and Partial Shading Conditions." In Advances in Intelligent Systems and Computing, 27–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7868-2_3.

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Ibnelouad, Aouatif, Abdeljalil El Kari, Hassan Ayad, and Mostafa Mjahed. "A Comprehensive Comparison of Two Behavior MPPT Techniques, the Conventional (Incremental Conductance (INC)) and Intelligent (Fuzzy Logic Controller (FLC)) for Photovoltaic Systems." In Modeling, Identification and Control Methods in Renewable Energy Systems, 47–84. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1945-7_3.

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Rebhi, Mhamed, Othmane Abdelkhalek, Bouchiba Bousmaha, and Mouaad Yaichi. "Variable Step Size Techniques for Conventional MPPT Algorithms." In Artificial Intelligence and Renewables Towards an Energy Transition, 822–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63846-7_79.

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Тези доповідей конференцій з теми "INTELLIGENT MPPT TECHNIQUES"

Yousra, Izgheche, and Bahi Tahar. "Wind Turbine MPPT Based on Intelligent Method." In 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE, 2023. http://dx.doi.org/10.1109/mi-sta57575.2023.10169165.

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El Telbany, Mohamed E., Ayman Youssef, and Abdelhalim Abdelnaby Zekry. "Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review." In 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology (ICAIET). IEEE, 2014. http://dx.doi.org/10.1109/icaiet.2014.13.

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Letha, Shimi Sudha, Tilak Thakur, Jagdish Kumar, Dnyaneshwar Karanjkar, and Santanu Chatterji. "Design and Real Time Simulation of Artificial Intelligent Based MPP Tracker for Photo-Voltaic System." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37967.

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Анотація:

This paper presents an Artificial Intelligent based Maximum Power Point Tracking (MPPT) of a photo-voltaic system implementation using dSPACE 1104. The paper also proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) / Constant Voltage Tracker (CVT) for a photovoltaic (PV) powered multilevel inverter which requires a fixed constant dc voltage at its input. The MPPT algorithms viz. perturb and observe, incremental conductance, neural network, ANFIS and ANFIS/CVT have been designed and implemented on laboratory prototype. The modeling of various MPPT algorithms have been done on MATLAB/SIMULINK platform. Real time simulations have been carried out using dSPACE R&D controller board and CONTROLDESK software. The performance comparisons of various MPPT techniques applied to stand-alone PV system with resistive load have been presented for varying solar radiation conditions. The authors hope that the comparative analysis presented in this work will be helpful for further research.

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Sunar, Mahima, C. Nithya, and J. Preetha Roselyn. "Study of intelligent MPPT controllers for a grid connected PV system." In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2017. http://dx.doi.org/10.1109/itcosp.2017.8303151.

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Santhiya, R., K. Deepika, R. Boopathi, and M. MohamedThameem Ansari. "Experimental Determination of MPPT using Solar Array Simulator." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951313.

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Azad, Murari Lal, Pradip Kumar Sadhu, and Soumya Das. "Comparative Study Between P&O and Incremental Conduction MPPT Techniques- A Review." In 2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2020. http://dx.doi.org/10.1109/iciem48762.2020.9160316.

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Ibnelouad, Aouatif, Abdeljalil El Kari, Hassan Ayad, and Mostafa Mjahed. "A comprehensive comparison of the classic and intelligent behavior MPPT techniques for PV systems." In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2017. http://dx.doi.org/10.1109/ssd.2017.8166966.

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Govindharaj, Arunprasad, and Anitha Mariappan. "Adaptive Neuralback Stepping Controller for MPPT in Photo Voltaic Systems." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951363.

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Prasad, Saradh, M. Ulaganathan, Devaraj Durairaj, Mamduh J. Aljaafreh, and MohamadS AlSalhi. "Implementation of MPPT for Flexible Solar Cells embedded on Unmanned Aerial Vehicles." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951417.

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John, Riby, S. Sheik Mohammed, and Richu Zachariah. "Variable step size Perturb and observe MPPT algorithm for standalone solar photovoltaic system." In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2017. http://dx.doi.org/10.1109/itcosp.2017.8303163.

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