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Journal articles on the topic 'MPPT SEPIC Neuro-Fuzzy PV System Fuzzy Logic'
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1
M., Ashwini, and K. Sekar Dr. "DEVELOPMENT OF GRID CONNECTED PHOTOVOLTAIC SYSTEM USING FUZZY LOGIC CONTROLLER." Journal of Engineering, Scientific Research and Applications 4, no. 2 (2018): 1–9. https://doi.org/10.5281/zenodo.1530074.
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This paper describes a Development of grid connected Photovoltaic (PV) system using Fuzzy Logic Controller (FLC). The Modified Single Ended Primary Inductor Converter (Mod. SEPIC) with the fuzzy logic based Maximum Power Point Tracking (MPPT) provides a constant voltage for a PV system. This converter is highly preferred to obtain a high voltage gain. The output of Mod-SEPIC converter is given to three phase Hex bridge inverter which helps to invert the incoming DC into AC. The proposed system is designed and monitored with an embedded controller which generates a control signal and activates the driver unit. The main objective of this paper deals with the PV system to supply required renewable power to the AC load and the excess power produced from the PV array can be fed into the grid. The excess during peak radiation period can be utilized effectively and fed to the grid thereby reduces the cost of the system. The whole system is developed and tested using MATLAB/Simulink software under various climatic and load conditions.
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Riahi, Jamel, Silvano Vergura, Dhafer Mezghani, and Abdelkader Mami. "Smart and Renewable Energy System to Power a Temperature-Controlled Greenhouse." Energies 14, no. 17 (2021): 5499. http://dx.doi.org/10.3390/en14175499.
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This paper presents the modeling and simulation of a Multi-Source Power System (MSPS)—composed of two renewable energy sources and supported by a Battery Energy Storage System (BESS)—to supply the ventilation and heating system for a temperature-controlled agricultural greenhouse. The first one is a photovoltaic (PV) generator connected to a DC/AC inverter and the second one is a wind turbine connected to a Permanent Magnet Synchronous Generator (PMSG). The temperature contribution in the model of the PV generator is deeply studied. A Maximum Power Point Tracking (MPPT) control based on fuzzy logic is used to drive a SEPIC converter to feed the maximum power to the greenhouse actuators. The operation of the actuators (ventilation and heating systems), on the basis of the mismatch between the internal temperature and the reference one, is controlled by a PI controller optimized by fuzzy logic, for more robust results. The simulation of the system is carried out in a Matlab/Simulink environment and its validation is based on the comparison between the simulated and experimental data for a test greenhouse, located in the Faculty of Science in Tunis. The results show that the proposed system provides an efficient solution for controlling the microclimate of the agricultural greenhouse in different periods of the year.
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Seguel, Julio López, Seleme I. Seleme, and Lenin M. F. Morais. "Comparative Study of Buck-Boost, SEPIC, Cuk and Zeta DC-DC Converters Using Different MPPT Methods for Photovoltaic Applications." Energies 15, no. 21 (2022): 7936. http://dx.doi.org/10.3390/en15217936.
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The power produced in a photovoltaic (PV) system is highly dependent on meteorological conditions and the features of the connected load. Therefore, maximum power point tracking (MPPT) methods are crucial to optimize the power delivered. An MPPT method needs a DC-DC converter for its implementation. The proper selection of both the MPPT technique and the power converter for a given scenario is one of the main challenges since they directly influence the overall efficiency of the PV system. This paper presents an exhaustive study of the performance of four step-down/step-up DC-DC converter topologies: Buck-Boost, SEPIC, Zeta and Cuk, using three of the most commonly implemented MPPT techniques: incremental conductance (IncCond), perturb and observe (P&O) and fuzzy logic controller (FLC). Unlike other works available in the literature, this study compares and discusses the performance of each MPPT/converter combination in terms of settling time and tracking efficiency of MPPT algorithms, and the conversion efficiency of power converters. Furthermore, this work jointly considers the effects of incident radiation variations, the temperature of the PV panel and the connected load. The main contribution of this work, other than selecting the best combination of converter and MPPT strategy applied to typical PV systems with DC-DC power converters, is to formulate a methodology of analysis to support the design of efficient PV systems. The results obtained from simulations performed in Simulink/MATLAB show that the FLC/Cuk set consistently achieved the highest levels of efficiency, and the FLC/Zeta combination presents the best transient behavior. The findings can be used as a valuable reference for the decision to implement a particular MPPT/converter configuration among those included in this study.
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Oliver, Jeba Singh, Prince Winston David, Praveen Kumar Balachandran, and Lucian Mihet-Popa. "Analysis of Grid-Interactive PV-Fed BLDC Pump Using Optimized MPPT in DC–DC Converters." Sustainability 14, no. 12 (2022): 7205. http://dx.doi.org/10.3390/su14127205.
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In solar photovoltaic (PV) system-based Brushless DC (BLDC) motors for water pumping application, the role of DC/DC converters is very important. In order to extract the maximum power from the PV array, an efficient DC/DC converter is essential at the intermediate stage. In this work, different DC/DC converter topologies suitable for BLDC motors are proposed. The converters are supported by an optimized maximum power point tracking system to provide a reliable operation. Recent optimization algorithms such as fuzzy logic, perturb and observe, grey wolf, and whale optimization are implemented with the PI controller in maximum power point tracking to maximize the conversion efficiency. The obtained results using SEPIC, LUO, and interleaved LUO converters provide a comparative study in the case of converter output, motor parameters, and grid output. The performance analysis on three different converters and multiple optimization methods are carried out. By analyzing the performance of different converter topologies, the interleaved LUO converter outperforms the other two converters with the results of a voltage gain ratio of 1:22, conversion efficiency of 98.3%, and grid current THD of 2.9%. Moreover, regarding the power quality aspect, the total harmonic distortion of the grid current is maintained below the IEEE-519 standard. In addition, the developed system has an advantage of operating both in stand-alone and grid-connected operation modes.
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Hole, Shreyas Rajendra, and Agam Das Goswami. "EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations." Science and Technology for Energy Transition 79 (2024): 8. http://dx.doi.org/10.2516/stet/2024002.
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With the increasing demand for renewable energy, solar power has emerged as a promising option for sustainable power generation. However, the effectiveness and efficiency of solar power systems rely on the ability to effectively manage their performance, making it essential to develop efficient control models. This paper proposes a novel ensemble predictive control model for solar deployments using bio-inspired optimizations to improve load-connected solar deployments’ performance. The proposed model integrates multiple control devices, including Maximum Power Point Tracker, Proportional-Integral-Derivative, Proportional-Integral, and Fuzzy Logic Controllers, to selectively control the solar Photovoltaic systems. The proposed model incorporates a predictive control operation utilizing an LSTM-GRU (Long Short-Term Memory-Gated Recurrent Unit) with the VARMA (Vector Auto-Regressive Moving Average) model, which can accurately predict the future power generation of the solar system. This feature can facilitate efficient energy management and increase the system’s performance for different use cases. Implement a SEPIC (Single Ended Primary Inductor Capacitor) converter design to improve the system’s overall efficiency levels. To validate the effectiveness of the proposed approach, the author conducted experiments using real-world data and compared the proposed results with other control strategies. The results demonstrate that the ensemble predictive control model based on bio-inspired optimizations outperforms the existing control models regarding accuracy, efficiency, and stability levels. The proposed model has the potential to significantly improve the performance of load-connected solar deployments, offering a more practical approach to solar power generation. The combination of predictive control operations with bio-inspired optimizations can facilitate the design of sustainable energy systems with higher efficiency and accuracy.
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Farah, Lotfi, Adel Haddouche, and Ali Haddouche. "Comparison between proposed fuzzy logic and ANFIS for MPPT control for photovoltaic system." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 2 (2020): 1065. http://dx.doi.org/10.11591/ijpeds.v11.i2.pp1065-1073.
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In this paper, a maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is achieved based on fuzzy logic controller (FLC) and compared with an anfis (neuro-fuzzy) based mppt controller, this method allies the abilities of artificial neural networks in learning and the power of fuzzy logic to handle imprecise data. Both methods are simulated using matlab/ simulink. The choise of power variation and the current variation as inputs of the proposed controllersreducesthe calculation. Both FLC and ANFIS based MPPTare tested in terms of steady state performance and the pv system dynamic.
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Lotfi, Farah, Haddouche Adel, and Haddouche Ali. "Comparison between proposed fuzzy logic and ANFIS for MPPT control for photovoltaic system." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 2 (2020): 1065–73. https://doi.org/10.11591/ijpeds.v11.i2.pp1065-1073.
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In this paper, a maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is achieved based on fuzzy logic controller (FLC) and compared with an anfis (neuro-fuzzy) based mppt controller, this method allies the abilities of artificial neural networks in learning and the power of fuzzy logic to handle imprecise data. Both methods are simulated using matlab/ simulink. The choise of power variation and the current variation as inputs of the proposed controllersreducesthe calculation. Both FLC and ANFIS based MPPTare tested in terms of steady state performance and the pv system dynamic.
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Elgharbi, Abdessamia, Dhafer Mezghani, and Abdelkader Mami. "Intelligent Control of a Photovoltaic Pumping System." Engineering, Technology & Applied Science Research 9, no. 5 (2019): 4689–94. https://doi.org/10.5281/zenodo.3510246.
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This paper presents the application of the Adaptive Neuro Fuzzy Inference System (ANFIS) to track the maximum power of a photovoltaic generator that feeds a motor-pump group unit through a Pulse Width Modulation (PWM) inverter powered by a Single Ended Primary Inductance Converter (SEPIC) installed in the laboratory. The ANFIS control is trained in different temperatures and irradiances and the maximum power point tracking system varies automatically the duty cycle of the SEPIC converter. The performance of the MPPT controller is tested in simulations in Matlab/Simulink.
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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 (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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Aouatif, Ibnelouad, Elkari Abdeljalil, Ayad Hassan, and Mjahed Mostafa. "A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 2 (2021): 1252–64. https://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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Solofanja Jeannie, Rajonirina, Razafimahenina Jean Marie, Andrianaharison Yvon, and Randriamasinoro Njakarison Menja. "COMPARATIVE STUDY OF MPPT BASED ON ARTIFICIAL INTELLIGENCE." International Journal of Advanced Research 9, no. 11 (2021): 625–39. http://dx.doi.org/10.21474/ijar01/13784.
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An MPPT or Maximum power point tracking command, associated with an intermediate adaptation stage, allows a photovoltaic generator (GPV) to operate in such a way as to continuously produce the maximum of its power. We present in this paper a new intelligent approach of a MPPT based on the hybrid and adaptive neuro-fuzzy network of ANFIS model. The latter is applied to a SEPIC* converter in order to extract at any time the maximum power available at the generator terminals and transfer it into the load, regardless of the sunshine variation as well as the temperature. The proposed method for a fixed and simple structure implements a Takagi-sugeno fuzzy system. Its performance will be confirmed by the comparison with the fuzzy logic command which is already known with its speed.
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MR., SWAPNIL L. DHAGE, and SANGITA B. PATIL MRS. "MAXIMUM POWER POINT TRACKING CONTROL FOR PHOTOVOLTAIC SYSTEMS: NEURO-FUZZY APPROACH." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 6 (2017): 76–82. https://doi.org/10.5281/zenodo.1438135.
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The proper function of photovoltaic (PV) systems needs the design of an maximum power point tracking system (MPPT) to draw out the maximum possible power from the photovoltaic array. This paper investigates the efficiency of a neuro-fuzzy logic controller over conventional ones intended to track the maximum power point (MPP). The proposed model used for simulation studies is implemented using Matlab/Simulink. A comparison between the classical Perturb and Observe (P&O) algorithm and fuzzy algorithm in terms of MPPT accuracy is provided. Results prove that the proposed model is simple, reliable and allows simulation under different operating conditions. https://journalnx.com/journal-article/20150358
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Mutlag, Ammar Hussein, Hussein Shareef, Azah Mohamed, Jamal Abd Ali, and Maytham S. Ahmed. "Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System." Applied Mechanics and Materials 785 (August 2015): 215–19. http://dx.doi.org/10.4028/www.scientific.net/amm.785.215.
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The maximum output power of a photovoltaic (PV) system with a DC-DC converter depends mainly on the solar irradiance (G) and the temperature (T). Therefore, a maximum power point tracking (MPPT) mechanism is required to improve the overall system. The conventional MPPT approaches such as the perturbation and observation (P&O) technique have difficulty in finding true maximum power point. Thus various intelligent MPPT systems such as fuzzy logic controllers (FLC) are recently introduced. In FLC based MPPT, selecting the type of the membership function (MF) and the number of the fuzzy sets (FS) is critical for better performance. Thus, in this paper various adaptive neuro fuzzy inference system (ANFIS) is utilized to automatically tune the FLC membership functions instead of adopting the trial and error method. To find suitable MF for FLC, ANFIS is developed in MATLAB/Simulink and the effect of different types MF investigated. Simulation result shows that the FLC with triangular MF and seven FS give the best result. The evaluation indices used in this study includes the maximum extracted energy, minimum standard deviation of the error, and minimum mean square error.
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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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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 (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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Boudouane, Meriem, Lahoussine Elmahni, Rachid Zriouile, and Soufyane Ait El Ouahab. "Advancing solar energy harvesting: Artificial intelligence approaches to maximum power point tracking." International Journal of Power Electronics and Drive Systems (IJPEDS) 16, no. 1 (2025): 55. https://doi.org/10.11591/ijpeds.v16.i1.pp55-69.
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This paper presents a comparative study of five maximum power point tracking (MPPT) control techniques in photovoltaic (PV) systems. The algorithms evaluated include classical methods, such as perturb and observe (P&O) and incremental conductance (IC), as well as intelligent approaches such as fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS). Intelligent methods provide faster response times and fewer oscillations around the maximum power point (MPP). The structure of the PV system includes a PV generator, load, and DC/DC boost converter driven by an MPPT controller. The performance of these techniques is analyzed under identical climatic conditions (same irradiation and temperature) in terms of efficiency, response time, response curve, accuracy in tracking the MPP, and others considered in this work. Simulations were performed using MATLAB-Simulink software, demonstrating that ANNs and ANFIS outperform traditional methods in dynamic environments, with FL being computationally intensive. P&O exhibited significant oscillations, while IC a showed slower tracking speed.
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Ranganai, T. Moyo, Y. Tabakov Pavel, and Moyo Sibusiso. "Comparative analysis of different computational intelligence techniques for maximum power point tracking of PV systems." Journal of Sustainable Energy 13, no. 1 (2022): 12–22. https://doi.org/10.5281/zenodo.7139169.
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<strong>The performance of a photovoltaic (PV) module can be improved by employing maximum power point tracking (MPPT) controllers. </strong><strong>MPPT controllers are algorithms that are included in PV battery charge controllers or inverters to extract the maximum available power from PV modules for any given temperature and irradiance. </strong><strong>Several studies report that the use of PV modules without MPPT controllers results in power losses, which ultimately results in the need to install more solar panels for the same power requirement. Numerous techniques of varying complexities have been proposed in the literature to solve the MPPT objective function. This paper presents a comparative analysis of three computational intelligence (CI) based MPPT techniques namely, the fuzzy logic (FL) based controller, artificial neural networks (ANN) based controller, adaptive neuro-fuzzy inference system (ANFIS) based controller and one conventional technique, the perturbation and observation (P&O) controller</strong><strong>. These MPPT controllers are designed, simulated and analysed in the MATLAB/Simulink environment. The performance of the studied MPPT techniques is evaluated under steady-state weather conditions, rapidly changing weather conditions and varying load conditions. CI-based MPPT controllers are found to be more efficient than the P&O controller. Moreover, the ANFIS-based MPPT controller shows an outstanding MPPT performance for all the scenarios studied.</strong>
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D. Thivya Prasad. "Advancements in Grid-Connected Solar Photovoltaic Systems: Control and Optimization with Modified SEPIC-Luo Converter." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1896–911. https://doi.org/10.52783/jisem.v10i3.8858.
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The growing global need for energy has spurred the widespread adoption of grid-connected Solar Photovoltaic (SPV) systems. These systems harness the advantages of photovoltaic power generation, such as environmental sustainability, low maintenance requirements and noise-free operation, positioning them as a prominent Renewable Energy Source (RES). This conceptual framework emphases on demonstrating and control design of a PV grid-tied system, integrating a Modified Single-Ended Primary Inductance (SEPIC) - Luo Converter. The central objectives of this work are to investigate the behavior of solar PV systems and to develop an efficient grid-connected solar power solution. To achieve these objectives, a Maximum Power Point Tracking (MPPT) circuit is designed, leveraging Red deer algorithm optimized Adaptive Neuro-Fuzzy Inference System (ANFIS). This innovative approach ensures the continuous optimization of power extraction from solar PV modules. The DC voltage generated by the PV system is subsequently directed to a Single-Phase Voltage Source Inverter (1ΦVSI) for conversion into AC voltage. The resulting AC voltage is then made available for various applications within the grid. Based on the simulation results, the algorithm efficiency is of 98.8% and the converter efficiency is found to be 98.92%. The MPPT efficiency is of 98.61%, which gives better optimization when compared to that of other algorithms. This framework encapsulates the advancements in SPV systems, offering a sustainable energy solution that aligns with the growing demand for clean and efficient power generation.
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Izadbakhsh, Maziar, Alireza Rezvani, and Majid Gandomkar. "Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System." Archives of Electrical Engineering 63, no. 4 (2014): 551–78. http://dx.doi.org/10.2478/aee-2014-0038.
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Abstract The microgrid (MG) technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG), photovoltaic (PV), microturbine generation (MTG) systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique was used by an adaptive neuro-fuzzy inference system (ANFIS); also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.
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Abidi, Hsen, Lilia Sidhom, and Ines Chihi. "Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques." Energies 16, no. 8 (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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Devarakonda, Ashwin Kumar, Natarajan Karuppiah, Tamilselvi Selvaraj, Praveen Kumar Balachandran, Ravivarman Shanmugasundaram, and Tomonobu Senjyu. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems." Energies 15, no. 22 (2022): 8776. http://dx.doi.org/10.3390/en15228776.
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The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.
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Kalaiarasi, N., A. Sivapriya, Pradeep Vishnuram, et al. "Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques." International Transactions on Electrical Energy Systems 2023 (September 27, 2023): 1–16. http://dx.doi.org/10.1155/2023/1134633.
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Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system’s performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted.
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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 (2021): 57. http://dx.doi.org/10.11591/ijece.v11i1.pp57-73.
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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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M., A. Abo-Sennah, A. El-Dabah M., and El-Biomey Mansour Ahmed. "Maximum power point tracking techniques for photovoltaic systems: a comparative study." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 57–73. https://doi.org/10.11591/ijece.v11i1.pp57-73.
Full textAbstract:
Photovoltaic (PV) systems 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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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 (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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Chnini, Khalil, Mahamadou Abdou Tankari, Houda Jouini, Hatem Allagui, Mostafa Ahmed Ibrahim, and Ezzeddine Touti. "Embedded Processor-in-the-Loop Implementation of ANFIS-Based Nonlinear MPPT Strategies for Photovoltaic Systems." Energies 18, no. 10 (2025): 2470. https://doi.org/10.3390/en18102470.
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The integration of photovoltaic (PV) systems into global energy production is rapidly expanding. However, achieving maximum power extraction remains a significant challenge due to the nonlinear electrical characteristics of PV modules, which are highly sensitive to environmental variations such as temperature fluctuations and irradiance changes. This study presents a structured design, testing, and quasi-experimental validation methodology for robust Maximum Power Point Tracking (MPPT) control in PV systems. We propose two advanced AI-based nonlinear control strategies: an Adaptive Neuro-Fuzzy Inference System combined with Fast Terminal Synergetic Control (ANFIS-FTSC) for a boost converter and ANFIS with Backstepping (ANFIS-BS) for a Single-Ended Primary Inductor Converter (SEPIC), both of which have demonstrated tracking efficiencies exceeding 99.6%. To evaluate real-time performance, a Processor-in-the-Loop (PIL) validation is conducted using an ARM-based STM32F407VG microcontroller. The methodology adheres to a Model-Based Design (MBD) framework, ensuring systematic development, implementation, and verification of the MPPT algorithms in an embedded environment. Experimental results demonstrate that the proposed controllers achieve high efficiency, rapid convergence, and robust maximum power point tracking under varying operating conditions. The successful PIL-based validation confirms the feasibility of these intelligent control techniques for real-world deployment in PV energy systems, paving the way for more efficient and adaptive renewable energy solutions.
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2Dr. V. Jayalakshmi, R. Sathyapriya ,. "Hres Integrated Dstatcom Using High Gain Sepic-Zeta Based Woa Optimized Anfis-Mppt Controller for Pq Issues." Journal of Electrical Systems 20, no. 1s (2024): 623–38. http://dx.doi.org/10.52783/jes.808.
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The power generated by renewable energy sources (RES), including photovoltaic (PV) and wind energy systems, are a great deal dependent on climate situations that result in Power Quality (PQ) issues, which require rapid adjustment of energy transmission and distribution systems. As a solution, (Distribution Static Synchronous Compensator) DSTATCOM is implemented for reactive power compensation, reducing voltage sag, swell, and THD. A novel High-gain SEPIC-ZETA converter is designed in this study to enhance the voltage obtained from the PV system with high efficiency. On the other hand, the proposed work aims to improve PQ issues using DSTATCOM with PV-Wind and battery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point (MPPT) controller is adopted for tracking optimal power from the PV and to tune the parameters of this controller adopting Whale optimization algorithm (WOA). Moreover, the PWM rectifier is employed to convert the AC supply from the wind energy system to DC and it is controlled by the Proportional Integral (PI) controller. Hysteresis Current Controller (HCC) is incorporated to generate reference current based on Synchronous Reference Frame (SRF) theory, which is required for the reduction of harmonics. Three phase voltage source inverter is integrated to convert DC-AC supply for distributing the energy supply to load application. Finally, the anticipated work is implemented in MATLAB/Simulink and the comparative analysis is made towards the conventional topologies to authenticate the proficiency of the developed work. As a consequence, the outcomes demonstrate the improved PQ with a lower THD value of 0.72% with high tracking efficiency is accomplished by the proposed technique.
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V. Jayalakshmi, R. Sathyapriya,. "HRES Integrated DSTATCOM Using High Gain Sepic-Zeta Based WOA Optimized ANFIS-MPPT Controller for PQ Issues." Journal of Electrical Systems 20, no. 2s (2024): 149–64. http://dx.doi.org/10.52783/jes.1118.
Full textAbstract:
The power generated by renewable energy sources (RES), including photovoltaic (PV) and wind energy systems, are a great deal dependent on climate situations that result in Power Quality (PQ) issues, which require rapid adjustment of energy transmission and distribution systems. As a solution, (Distribution Static Synchronous Compensator) DSTATCOM is implemented for reactive power compensation, reducing voltage sag, swell, and THD. A novel High-gain SEPIC-ZETA converter is designed in this study to enhance the voltage obtained from the PV system with high efficiency. On the other hand, the proposed work aims to improve PQ issues using DSTATCOM with PV-Wind and battery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point (MPPT) controller is adopted for tracking optimal power from the PV and to tune the parameters of this controller adopting Whale optimization algorithm (WOA). Moreover, the PWM rectifier is employed to convert the AC supply from the wind energy system to DC and it is controlled by the Proportional Integral (PI) controller. Hysteresis Current Controller (HCC) is incorporated to generate reference current based on Synchronous Reference Frame (SRF) theory, which is required for the reduction of harmonics. Three phase voltage source inverter is integrated to convert DC-AC supply for distributing the energy supply to load application. Finally, the anticipated work is implemented in MATLAB/Simulink and the comparative analysis is made towards the conventional topologies to authenticate the proficiency of the developed work. As a consequence, the outcomes demonstrate the improved PQ with a lower THD value of 0.72% with high tracking efficiency is accomplished by the proposed technique.
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Bataineh, Khaled, and Naser Eid. "A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions." Resources 7, no. 4 (2018): 68. http://dx.doi.org/10.3390/resources7040068.
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A hybrid MPPT (maximum power point tracking) controller integrates FLC (fuzzy logic controller) and P&O (Perturbation and Observation) method for MMPT of PV (Photovoltaic) under dynamic weather conditions is proposed. An adaptive neuro-fuzzy inference system is used to optimize parameters and membership functions of FLC. FLC is used to find the region of MPP (maximum power point); then, P&O technique is employed to accurately track the MPP. MATLAB/Simulink models are built to evaluate the performance of the proposed hybrid algorithm. In order to validate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is tested against dynamic weather condition. The results showed that the proposed algorithm successfully improve the dynamic and steady state responses of PV under severe dynamic weather condition. More specifically, the proposed approach shows its capability to attain the MPP faster than P&O and provided higher power than the standalone FLC. Finally, the proposed algorithm overcomes the limitations associated with FLC and P&O.
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Arulmurugan, R., and Swapna Sandaraju. "An anfis converter control approach of grid connected wind/PV/battery system." IAES International Journal of Robotics and Automation (IJRA) 8, no. 1 (2019): 6–17. https://doi.org/10.11591/ijra.v8i1.pp6-17.
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The major objective of this task is a control technique for power flow management of a network associated hybrid PV-wind-battery based system with multi-input transformer coupled bidirectional dc-dc converter using ANFIS controller is introduced. The proposed system intends to fulfill the demand of load, deal with the power flow from the distinctive sources, injects surplus power into the network and charge the battery from framework as and when required. A transformer coupled boost half-bridge converter is used to tackle power from wind, while bidirectional buck-boost converter is used to outfit control from PV alongside battery charging/releasing control. A single stage full-bridge bidirectional converter is used for ac loads and collaboration with grid. The proposed converter has reduced number of intensity change stages. This enhances the effectiveness and unwavering quality of the framework. In this article Adaptive Neuro Fuzzy interference System (ANFIS) is proposed for better performance of the system. Neural system has many inputs and also has multiple outputs but the fuzzy logic has multiple inputs and single output, so the combination of this two is known as ANFIS which is utilized for nonlinear applications. The proposed paper of simulation results acquired using MATLAB/Simulink demonstrate the execution of the proposed control procedure for power stream the executives under different methods of activity.
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Rajonirina, Solofanja Jeannie, Jean Marie Razafimahenina, Yvon Andrianaharison, and Njakarison Menja Randriamasinoro. "COMPARATIVE STUDY OF MPPT BASED ON ARTIFICIAL INTELLIGENCE." November 16, 2021. https://doi.org/10.5281/zenodo.5767202.
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An MPPT or Maximum power point tracking command, associated with an intermediate adaptation stage, allows a photovoltaic generator (GPV) to operate in such a way as to continuously produce the maximum of its power. We present in this paper a new intelligent approach of a MPPT based on the hybrid and adaptive neuro-fuzzy network of ANFIS model. The latter is applied to a SEPIC* converter in order to extract at any time the maximum power available at the generator terminals and transfer it into the load, regardless of the sunshine variation as well as the temperature. The proposed method for a fixed and simple structure implements a Takagi-sugeno fuzzy system. Its performance will be confirmed by the comparison with the fuzzy logic command which is already known with its speed.
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"Photo Voltaic Mppt and Inverter using Fuzzy Logic." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 3981–84. http://dx.doi.org/10.35940/ijrte.c5457.098319.
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In this paper the photo voltaic (i.e. PV) system is used with the SEPIC converter (i.e. single ended primary inductor converter) and MPPT (maximum power point tracker) by using fuzzy logic. The fuzzy logic controller is used for gaining high efficiency by outputting crisp values to the SEPIC for converting dc to dc that is a buck/boost converter for converting dc voltage level. The inverter is used for transforming dc to ac and reducing harmonics for better ac output.
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"MPPT Controller Based on Fuzzy Logic for Photovoltaic Systems using Line-Commutated Inverter and Sepic Converter." International Journal of Innovative Technology and Exploring Engineering 8, no. 10S (2019): 41–47. http://dx.doi.org/10.35940/ijitee.j1007.08810s19.
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A controller for maximum power point tracking (MPPT) based on fuzzy logic was developed to connect the solar panels and three phase grid through an inverter. MPPT controller traces the maximum power and then feeds this power to the three phase grid irrespective of the changes in solar irradiations. The input and output variables for the fuzzy logic controller were selected in order to vary the inverter firing angle to track the maximum power from the solar panels. The proposed system using fuzzy logic controller was built using MATLAB Simulink /Power System Block (PSB) set. A DSP controller has been embedded with program for firing of the thyristors used in the inverter with appropriate pulses. Hardware of the entire system was fabricated in the laboratory and the outputs from the PV array of 90 volts, 11 amperes are exhibited. Both the hardware and simulation outputs have been compared which are in close agreement to validate the suggested system.
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Pavithra, C., Vidhyareni S, Vijayadharshini M, Shree Akshaya K B, and Varsha N. "Comparison of Solar P&O and FLC-based MPPT Controllers & Analysis under Dynamic Conditions." EAI Endorsed Transactions on Energy Web 11 (January 31, 2024). http://dx.doi.org/10.4108/ew.4988.
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Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].
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"Hybrid PV and Wind Energy for Grid Connected System using SEPIC Converter." International Journal of Innovative Technology and Exploring Engineering 9, no. 2S4 (2019): 552–55. http://dx.doi.org/10.35940/ijitee.b1198.1292s419.
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A hybrid generator is designed for both wind energy and photovoltaic system (PV) based on permanent magnet synchronous generator to maintain dc link voltage. In this paper, both the sources are connected with grid through a single ended primary inductor converter (SEPIC) and three phase inverter is followed by SEPIC converter. The fuzzy logic controller used to provide gate pulses for the converter and model predictive controller is providing pulses to three phase inverter which tracking the highest power from the wind and PV system. In the PV system the maximum power point tracking (MPPT) method is used to get higher power from the source. This new hybrid system operation is done by both the effective controllers and system steady is achieved. The integrated energy with high efficiency is directed to grid for distribution and the system is verified in Matlab/Simulink and simulation results are given with model.
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Riahi, Jamel, Silvano Vergura, Dhafer Mezghani, and Abdelkader Mami. "Smart and Renewable Energy System to Power a Temperature-Controlled Greenhouse." September 6, 2021. https://doi.org/10.3390/en14175499.
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This paper presents the modeling and simulation of a Multi-Source Power System (MSPS)—composed of two renewable energy sources and supported by a Battery Energy Storage System (BESS)—to supply the ventilation and heating system for a temperature-controlled agricultural greenhouse. The first one is a photovoltaic (PV) generator connected to a DC/AC inverter and the second one is a wind turbine connected to a Permanent Magnet Synchronous Generator (PMSG). The temperature contribution in the model of the PV generator is deeply studied. A Maximum Power Point Tracking (MPPT) control based on fuzzy logic is used to drive a SEPIC converter to feed the maximum power to the greenhouse actuators. The operation of the actuators (ventilation and heating systems), on the basis of the mismatch between the internal temperature and the reference one, is controlled by a PI controller optimized by fuzzy logic, for more robust results. The simulation of the system is carried out in a Matlab/Simulink environment and its validation is based on the comparison between the simulated and experimental data for a test greenhouse, located in the Faculty of Science in Tunis. The results show that the proposed system provides an efficient solution for controlling the microclimate of the agricultural greenhouse in different periods of the year.
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"COMPARATIVE ANALYSIS OF CONVENTIONAL, ARTIFICIAL INTELLIGENCE AND HYBRID BASED MPPT TECHNIQUE FOR 852.6-WATT PV SYSTEM." International Journal of Social Ecology and Sustainable Development 13, no. 2 (2022): 0. http://dx.doi.org/10.4018/ijsesd.302463.
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In this article, Matlab & Simulation software is used for analysis and comparison of 8(Eight) different MPPT. Different MPPT techniques that have been considered in this article are PWM-based, Perturb and Observation (P&O), Incremental Conductance (InC), and Modified InC (MIC) that comes under the Conventional Method. In the Artificial Intelligence, Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN) is chosen and in Hybrid method Neuro-Fuzzy Network (NFN) and Adaptive Neural Fuzzy Inference System (ANFIS) has been considered. PV module of 852.2 Watt is designed with the Boost Converter which can boost the voltage up to 185 Volt for all MPPT. A set of data has been taken for FLC, ANN, NFN, and ANFIS. After implementation, the result has been analyzed for standard test conditions and for the different environmental conditions. In this article, both irradiation and the temperature have been varied together for all MPPT rest are kept constant.
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Moyo, Ranganai T., Pavel Y. Tabakov, and Sibusiso Moyo. "Design and Modeling of the ANFIS-Based MPPT Controller for a Solar Photovoltaic System." Journal of Solar Energy Engineering 143, no. 4 (2020). http://dx.doi.org/10.1115/1.4048882.
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Abstract Maximum power point tracking (MPPT) controllers play an important role in improving the efficiency of solar photovoltaic (SPV) modules. These controllers achieve maximum power transfer from PV modules through impedance matching between the PV modules and the load connected. Several MPPT techniques have been proposed for searching the optimal matching between the PV module and load resistance. These techniques vary in complexity, tracking speed, cost, accuracy, sensor, and hardware requirements. This paper presents the design and modeling of the adaptive neuro-fuzzy inference system (ANFIS)-based MPPT controller. The design consists of a PV module, ANFIS reference model, DC–DC boost converter, and the fuzzy logic (FL) power controller for generating the control signal for the converter. The performance of the proposed ANFIS-based MPPT controller is evaluated through simulations in the matlab/simulink environment. The simulation results demonstrated the effectiveness of the proposed technique since the controller can extract the maximum available power for both steady-state and varying weather conditions. Moreover, a comparative study between the proposed ANFIS-based MPPT controller and the commonly used, perturbation and observation (P&O) MPPT technique is presented. The simulation results reveal that the proposed ANFIS-based MPPT controller is more efficient than the P&O method since it shows a better dynamic response with few oscillations about the maximum power point (MPP). In addition, the proposed FL power controller for generating the duty cycle of the DC–DC boost converter also gave satisfying results for MPPT.
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Sudiharto, Indhana, Eka Prasetyono, Anang Budikarso, and Safira FitriaDevi. "A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm." Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, August 30, 2022. http://dx.doi.org/10.22219/kinetik.v7i3.1452.
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 Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.
 
 
 
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"An Effective Battery Energy Management System in Hybrid Solar/Wind System using ANFIS Controlled Bi-Directional DC-DC Converter." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 4150–58. http://dx.doi.org/10.35940/ijrte.d6876.118419.
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This paper proposes and evaluates an adaptive neuro-fuzzy inference system (ANFIS) based battery energy management system (BEMS). The proposed configuration consists of photovoltaic (PV) and wind energy conversion system (WECS) based hybrid renewable energy system as the primary source and battery system as the energy storage device. The all the primary sources is connected to the DC bus by a DC/DC converter whereas, Battery storage system is connected using Bi-Directional system for charging and discharging purpose. An ANFIS based supervisory control system is proposed in this paper which determines effective battery management system by analyzing the power demand by the load and the state of charge (SOC) of the battery furthermore, an fuzzy logic controller (FLC) based maximum power point tracking (MPPT) is used in the PV and wind energy conversion system (WECS) to track the maximum available power for the different irradiance and wind velocity respectively. The obtained results are compared with the fuzzy logic-based energy management system to test the effectiveness of the system. A 500 W PV system and a 500 W Permanent magnet synchronous generator (PMSG) based WECS is implemented for its simplicity and high efficiency. The proposed control topology is designed and tested using MATLAB/Simulink.
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Paradesi, Sowmya, and Bhaskar Khanna. "Control of Grid Connected PV Inverter Acting as STATCOM for Reactive Power Compensation using ANFIS MPPT and Enhanced SPWM." June 7, 2025. https://doi.org/10.5281/zenodo.15115730.
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This paper analyses a grid-connected photovoltaic (PV) inverter system with a dual-stage control framework to enhance energy efficiency and grid compatibility. A MATLAB/Simulink model is developed to evaluate the system’s performance, incorporating a Single-Ended Primary Inductor Converter (SEPIC) for optimized energy transfer. The system uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) for improved Maximum Power Point Tracking (MPPT), providing faster convergence and reduced steady state oscillations. The inverter employs an enhanced Sinusoidal Pulse Width Modulation (SPWM) technique, minimizing Total Harmonic Distortion (THD) compared to conventional space vector PWM. Grid Synchronization and regulation of the AC current are achieved through Phase-Locked Loop (PLL), ensuring precise phase and frequency alignment. Simulation results validate the system’s stability, rapid dynamic response and effective energy harvesting.
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Paradesi, Sowmya, and Bhaskar Kanna. "Control of Grid Connected PV Inverter Acting as STATCOM for Reactive Power Compensation using ANFIS MPPT and Enhanced SPWM." June 7, 2025. https://doi.org/10.5281/zenodo.15220714.
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This paper analyses a grid-connected photovoltaic (PV) inverter system with a dual-stage control framework to enhance energy efficiency and grid compatibility. A MATLAB/Simulink model is developed to evaluate the system’s performance, incorporating a Single-Ended Primary Inductor Converter (SEPIC) for optimized energy transfer. The system uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) for improved Maximum Power Point Tracking (MPPT), providing faster convergence and reduced steady state oscillations. The inverter employs an enhanced Sinusoidal Pulse Width Modulation (SPWM) technique, minimizing Total Harmonic Distortion (THD) compared to conventional space vector PWM. Grid Synchronization and regulation of the AC current are achieved through Phase-Locked Loop (PLL), ensuring precise phase and frequency alignment. Simulation results validate the system’s stability, rapid dynamic response and effective energy harvesting.
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Aifan G. Alsulami, Abdulellah, Abdullah Ali Alhussainy, Ahmed Allehyani, et al. "A comparison of several maximum power point tracking algorithms for a photovoltaic power system." Frontiers in Energy Research 12 (May 27, 2024). http://dx.doi.org/10.3389/fenrg.2024.1413252.
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This paper presents a comparative study between traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for Photovoltaic (PV) powered DC Shunt Motors. Given the nonlinearity of PV systems, they require nonstandard approaches to harness their full potential. Each PV module has a unique maximum power point on its IV curve due to its nonlinear characteristic nature. Power electronic converters are utilized to enable operation at that point. There are many different algorithms described in the introduction, each with its have their own advantages and drawbacks. Recognizing the potential enhancement of PV system efficiency through effective Maximum Power Point (MPP) tracking, this paper evaluates five MPPT methods under varying DC loads. The five algorithms will be as follows: Incremental Conductance and Perturb and Observe as traditional algorithms. Fuzzy Logic Control, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference Systems as Intelligent Algorithms. While traditional algorithms generally produced acceptable results except for Perturb &amp; Observe, intelligent algorithms performed well under rapidly changing solar radiation conditions. Due to inadequate data, intelligent algorithms relying on data training struggled to track the maximum power point when the temperature changed due to inadequate data used for the training. The analysis focuses on the time required by each method to reach peak power under different load conditions, solar irradiance, and temperature variations. The advantages and disadvantages of each MPPT with a shunt DC motor are detailed in the comparative study.
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Jegajothi, B., I. Kathir, Neeraj Kumar Shukla, and R. B. R. Prakash. "Quasi-oppositional artificial algae optimization with adaptive neuro fuzzy inference based maximum power point tracking for PV systems." Journal of Intelligent & Fuzzy Systems, July 5, 2023, 1–15. http://dx.doi.org/10.3233/jifs-223889.
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Because of environmental issues and energy crises, significant attention has been received in the domain of renewable and clean energy systems. Solar energy is the most effective source of renewable energy technologies. Recently, photovoltaic (PV) system have become common in grid-linked applications and plays a vital part in power production. MPPT algorithms enable PV systems to capture the maximum available power from the solar panels, regardless of variations in solar irradiance, temperature, and other environmental factors. By continuously tracking the MPP, MPPT techniques ensure that the PV system operates at its highest efficiency, resulting in increased energy harvesting and improved overall performance. Meanwhile, the frequent modifications in irradiance and temperature pose a major challenging issue which can be resolved by the use of artificial intelligence MPPT methodologies like artificial neural networks (ANN), fuzzy logic (FL), and metaheuristics systems. In this aspect, this work presents a new quasi-oppositional artificial algae optimization (QOAAO) with an adaptive neuro-fuzzy inference system (ANFIS) technique, named QOAAO-ANFIS for maximum efficiency MPPT technique for minimizing the present ripple and power oscillations over the MPP. The presented QOAAO-ANFIS model mainly depends upon the integration of the ANFIS and QOHOA techniques. In addition, the presented QOAAO-ANFIS model involves optimal MF selection of the ANFIS model to estimate the irradiation level and compute PV voltage equivalent to maximal power point. The QOAAO model can be utilized for enhancing the optimization process of membership function variables under varying conditions and awareness of global optima. The simulation result analysis of the QOAAO-ANFIS model takes place in terms of different evaluation measures. Extensive comparative results reported the better performance of the QOAAO-ANFIS model with maximum tracking efficiency of 99.89% and a minimum convergence time of 13.51 ms.