Academic literature on the topic 'MPPT SEPIC Neuro-Fuzzy PV System Fuzzy Logic'

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Journal articles on the topic "MPPT SEPIC Neuro-Fuzzy PV System Fuzzy Logic"

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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2

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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8

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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9

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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Abstract:
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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Conference papers on the topic "MPPT SEPIC Neuro-Fuzzy PV System Fuzzy Logic"

1

B, Kavya Santhoshi, Ravi Kishore D, Nivesh Arja, V. V. Satya Sai Naga Geethika Baladari, and Chaitanya Kumar Guttula. "High Gain KY Converter For Grid Tied Clean Energy PV System Using Cascaded Neuro Fuzzy Logic MPPT Algorithm." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921792.

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