Journal articles: 'Optimum power point tracking'

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EL-Saady Ahmed, Gaber, EL-Noby Ahmed Ibrahim, and Hazem Hassan Ali. "Optimum Power Point Tracking of Variable Speed Wind Turbine DFIG using Genetic Algorithm." International Journal of Engineering Trends and Technology 37, no. 7 (2016): 373–83. http://dx.doi.org/10.14445/22315381/ijett-v37p264.

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Pintilie, Lucian Nicolae, Horia Cornel Hedeșiu, Călin Gheorghe Rusu, et al. "Energy Conversion Optimization Method in Nano-Grids Using Variable Supply Voltage Adjustment Strategy Based on a Novel Inverse Maximum Power Point Tracking Technique (iMPPT)." Electricity 4, no. 4 (2023): 277–308. http://dx.doi.org/10.3390/electricity4040017.

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This paper introduces a novel power supply voltage adjustment strategy that can determine the optimum voltage value based on the amount of absorbed power. The novel automatic voltage adjustment technique was called inverse maximum power point tracking (iMPPT). The proposed control strategy consists of a modified maximum power point tracking (MPPT) algorithm (more precisely the P&O method). In this case, the modified MPPT technique establishes the minimum value of the input absorbed power of a consumer load served by a switched-mode power supply (SMPS). The iMPPT adjusts the input power by modifying the input voltage of the main power supply. The served loads are connected to the variable power supply via an interfacing power electronics converter that performs the automatic voltage regulation function (AVR). The optimal value of the input voltage level can be achieved when the input power of the automatic voltage regulation converter is at a minimum. In that case, the energy conversion efficiency ratio is at a maximum, and the overall losses related to the front-end power stage are at a minimum. The proposed technique can also be considered a Maximum Efficiency Tracking (MET) method. By performing the inverse operation of a maximum power point tracking algorithm on the input demanded power of a switched mode power supply (SMPS), the optimum input voltage level can be determined when the maximum energy conversion ratio (related to a given load level) is achieved. The novel proposed iMPPT method can improve the energy conversion ratio from 85% up to approximately 10% in the case of an output power level of 800 W served by a synchronous buck converter at the input voltage level of 350 V. The total amount of recovered power in this situation can be approximately 100 W.

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Alam, Afroz, Preeti Verma, Mohd Tariq, et al. "Jellyfish Search Optimization Algorithm for MPP Tracking of PV System." Sustainability 13, no. 21 (2021): 11736. http://dx.doi.org/10.3390/su132111736.

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Because of the rapid increase in the depletion rate of conventional energy sources, the energy crisis has become a central problem in the contemporary world. This issue opens the gateway for exploring and developing renewable energy sources to fulfill the exigent energy demand. Solar energy is an abundant source of sustainable energy and hence, nowadays, solar photovoltaic (PV) systems are employed to extract energy from solar irradiation. However, the PV systems need to work at the maximum power point (MPP) to exploit the highest accessible power during varying operating conditions. For this reason, maximum power point tracking (MPPT) algorithms are used to track the optimum power point. Furthermore, the efficient utilization of PV systems is hindered by renowned partial shading conditions (PSC), which generate multiple peaks in the power-voltage characteristic of the PV array. Thus, this article addresses the performance of the newly developed jellyfish search optimization (JSO) strategy in the PV frameworks to follow the global maximum power point (GMPP) under PSC.

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Shi, Ji Ying, Kai Zhang, and Yong Ge Zhang. "The New Technology of PV Power Generation System Based on Extremum Seeking Control." Advanced Materials Research 989-994 (July 2014): 1185–88. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1185.

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A novel is based on a simplified two-diode model of photovoltaic (PV) module,which can more accurately predict the output characteristic of the solar cells I-U curve. On this basis, Aiming at promptness and stability tracking the maximum power point of the photovoltaic cells under the conditions of the constant temperature and light intensity, a novel control strategy based on sliding mode based extremum seeking control (SM-ESC) system for optimally tracking the maximum power point of the photovoltaic cells is proposed.To achieve higher control quality, an adaptive adjustment method is further presented on the basic of stabilizing conditions of SM-ESC system, avoiding complicated manually parameter adjustment.Thus photovoltaic system can quickly stabilize at the maximum power point, less volatile when tracking the optimum output curve. Simulation results show the effectiveness and validity of the control strategy and adjustment method proposed .

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Mauledoux, Mauticio, Santiago Fernández Posada, and Oscar Avilés Sánchez. "Design and Implementation of a Neural Network Applied to the Maximum Power Point Tracking of a Solar Panel." Applied Mechanics and Materials 823 (January 2016): 383–88. http://dx.doi.org/10.4028/www.scientific.net/amm.823.383.

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This paper shows the design and implementation of a neural network using back propagation method in order to perform the tracking of the maximum power point of a solar panel; this can be achieved by the use of the predictive ability of the network which uses light sensors to perform angular movement of the panel to find the optimum position. Tests were performed both in artificial and real environments; the network tracking sensitivity was tested in the artificial environment and it gave a result of less than 8 degrees of error, on the other hand in terms of voltage an improvement of more than 25% was found on the tracking configuration against an static configuration. As for the real environment testing, the tracking achieved to find the value of maximum power with a difference smaller than 4% of the maximum power measurement obtained.

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He, Sijiang, and Hao Li. "Research on Maximum Power Tracking of Grey Wolf Optimization Algorithm Introducing Levy Flight." Journal of Physics: Conference Series 2303, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2303/1/012006.

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Abstract In the case of partial shadow, the power-voltage characteristic curve of photovoltaic array output will show multi-peak phenomenon, the traditional maximum power point tracking method is easy to fall into the local maximum power point, resulting in the loss of output power. Aiming at the common problems of traditional swarm intelligence optimization algorithms, such as slow convergence speed, large oscillation amplitude, and easy to fall into local optimum, a control method of gray wolf optimization algorithm with Levy flight was proposed. The algorithm uses GWO’s excellent ability of fast convergence speed and high solution accuracy to quickly converge to the vicinity of the maximum power point, and then uses CSA to achieve a stable state near the maximum power point. The power fluctuation is smaller. The simulation statistics show that compared with the traditional The improved gray wolf optimization algorithm has higher solution accuracy, shorter tracking time, and improves the output efficiency of the photovoltaic array.

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Zine, Mahmoud, Chouaib Labiod, Malika Ikhlef, Kamel Srairi, and Mohamed Benbouzid. "Improving Efficiency and Power Output of Switched Reluctance Generators through Optimum Operating Parameters." Machines 11, no. 8 (2023): 816. http://dx.doi.org/10.3390/machines11080816.

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The optimization of energy production in renewable energy systems is crucial to improve energy efficiency. In this context, the aim of this study focuses on maximizing the efficiency of a switched reluctance generator. This paper presents a novel approach to enhance the electrical power and efficiency of a switched reluctance generator by determining the optimal operating parameters based on the mechanical input power of the system. The proposed strategy consists of the following steps: First, an algorithm was developed that provides machine data for different power modes based on control parameters, including electrical and mechanical powers such as speed, torque, and turn-on and turn-off angles. In the next step, the obtained data were analyzed to identify the optimum points corresponding to the states with maximum power and efficiency for various scenarios. An algorithm for maximum power point tracking was also developed to determine the optimal parameters as a function of mechanical energy. Finally, the data and algorithms were integrated into the switched reluctance generator control system. Simulations were conducted to compare the proposed MPPT technique with other techniques. This comparison is essential to validate the effectiveness of the proposed strategy in achieving enhanced electrical power generation efficiency.

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Orellana, Cristian, Viviana Moya, Marcelo Moya, and Cristina Oscullo. "Artificial Intelligence-Controlled Photovoltaic Generator for Optimized Power Point Tracking." E3S Web of Conferences 532 (2024): 01002. http://dx.doi.org/10.1051/e3sconf/202453201002.

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This paper addresses the pressing need for sustainable energy solutions by focusing on developing a photovoltaic solar tracker enhanced with artificial intelligence (AI). The current and future global trends challenge energy systems to improve their output while also maintaining an eco-friendly approach, and there is an option to offset carbon emissions through photovoltaic energy. Nevertheless, the solar panel’s efficiency depends upon its ability to follow the sun’s movement to find the optimum energy angle. This project offers a unique solution, adopting a neural network technique that was trained using weather data from the daily weather forecasts to determine the correct angles of the panel at all times. The sampling unit was fabricated using aluminium and PLA materials and monitoring parameters of temperature, humidity, radiation, pressure, and atmospheric variables. A web-based interface lets monitor the system in oh-so-real-time and delivers graphical presentations of crucial metrics, including voltage, current, and power production. The outcomes suggest a significant enhancement in energy output, which ascends from 22.65% to 29.25%, equivalent to a 144.56 kWh-year rise. Although the margins of profitability may differ by region, our study sheds light on the efficiency of this AI-integrated solar tracker, especially in regions like Brazil or Spain, which facilitates alternative energy policies with possible economic benefits.

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Touhami, Ghaitaoui, Laribi Sliman, Arama Fatima Zohra, Harrouz Abdelkader, and Drici Khalil. "Extraction of Maximum Power of Organic Photovoltaic Generator Using MPPT Technique." Applied Mechanics and Materials 905 (February 15, 2022): 1–6. http://dx.doi.org/10.4028/p-75cf24.

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The amount of energy generated by an Organic Photovoltaic (OPV) system depends mainly on the following.Such as solar temperatures and irradiations. Depending on the high cost and low efficiency of an organic photovoltaic system, it can be operated at the maximum power point (MPPT) that changes with solar radiation, temperature or load variations. This work presents an improved algorithm for tracking the maximum power point (MPPT) of a OPV system under real climatic conditions. The proposed MPPT is based on the perturbation and observation (P&O) strategy and the variable pitch method which controls the load voltage to ensure optimum operating points of a OPV system.

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Alireza, Rezvani, Gandomkar Majid, izadbakhsh Maziar, and Vafaei Saeed. "Improvement of Grid-Connected Photovoltaic System Using Artificial Neural Network and Genetic Algorithm Under Different Condition." International Journal of Soft Computing, Mathematics and Control (IJSCMC) 3, no. 4 (2014): 15 to 32. https://doi.org/10.5281/zenodo.3889363.

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Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.

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Takruri, Maen, Maissa Farhat, Oscar Barambones, et al. "Maximum Power Point Tracking of PV System Based on Machine Learning." Energies 13, no. 3 (2020): 692. http://dx.doi.org/10.3390/en13030692.

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This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

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Maimun, Maimun, and Subhan Subhan. "Different Techniques of Multiple Power Point Tracking for Photovoltaic Systems." Jurnal Litek : Jurnal Listrik Telekomunikasi Elektronika 19, no. 1 (2022): 24. http://dx.doi.org/10.30811/litek.v19i1.2887.

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Understanding in such way the maximum available power generated by the photovoltaic (PV) array varies with the weather is critical for improving system efficiency by encouraging the PV system to operate at that maximum power point (MPP). Therefore, to maintain optimum power functioning at all irradiance levels and temperatures, a Maximum Power Point Tracking (MPPT) system is necessary. MPPT methods have been developed and implemented in a number of studies. The accuracy, convergence speed, ease of hardware implementation, PV dependency, number of necessary sensors, which are significantly differ across these systems. The first technique to be introduced was a single MPPT technique. However, as it works independently, it was unable to achieve several of the required characteristics. Afterwards, the merger techniques of multiple MPPTs and the combination of both (single and multiple MPPTs’ techniques) due to integrate the benefits of each algorithm while removing their limitations. Comparing and surveying MPPT algorithms in general took a significant amount of time. Despite this, there is a limited literature examining the combination techniques towards the multiple MPPT techniques and the single one. This paper presents the work and uses MATLAB/Simulink platform to simulate it. It is based on a study that contrasts single MPPT techniques with different combinations, namely the constant voltage (CV) method, the perturb and observe (PO) method and the combination of both (CV+PO), in order to validate MPPT's better performance.

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Maimun, Maimun, and Subhan Subhan. "Different Techniques of Multiple Power Point Tracking for Photovoltaic Systems." Jurnal Litek : Jurnal Listrik Telekomunikasi Elektronika 19, no. 1 (2022): 24. http://dx.doi.org/10.30811/litek.v19i1.2887.

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Understanding in such way the maximum available power generated by the photovoltaic (PV) array varies with the weather is critical for improving system efficiency by encouraging the PV system to operate at that maximum power point (MPP). Therefore, to maintain optimum power functioning at all irradiance levels and temperatures, a Maximum Power Point Tracking (MPPT) system is necessary. MPPT methods have been developed and implemented in a number of studies. The accuracy, convergence speed, ease of hardware implementation, PV dependency, number of necessary sensors, which are significantly differ across these systems. The first technique to be introduced was a single MPPT technique. However, as it works independently, it was unable to achieve several of the required characteristics. Afterwards, the merger techniques of multiple MPPTs and the combination of both (single and multiple MPPTs’ techniques) due to integrate the benefits of each algorithm while removing their limitations. Comparing and surveying MPPT algorithms in general took a significant amount of time. Despite this, there is a limited literature examining the combination techniques towards the multiple MPPT techniques and the single one. This paper presents the work and uses MATLAB/Simulink platform to simulate it. It is based on a study that contrasts single MPPT techniques with different combinations, namely the constant voltage (CV) method, the perturb and observe (PO) method and the combination of both (CV+PO), in order to validate MPPT's better performance.

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Maimun, Maimun, and Subhan Subhan. "Different Techniques of Multiple Power Point Tracking for Photovoltaic Systems." Jurnal Litek : Jurnal Listrik Telekomunikasi Elektronika 19, no. 1 (2022): 24–27. http://dx.doi.org/10.30811/litek.v19i1.21.

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Understanding in such way the maximum available power generated by the photovoltaic (PV) array varies with the weather is critical for improving system efficiency by encouraging the PV system to operate at that maximum power point (MPP). Therefore, to maintain optimum power functioning at all irradiance levels and temperatures, a Maximum Power Point Tracking (MPPT) system is necessary. MPPT methods have been developed and implemented in a number of studies. The accuracy, convergence speed, ease of hardware implementation, PV dependency, number of necessary sensors, which are significantly differ across these systems. The first technique to be introduced was a single MPPT technique. However, as it works independently, it was unable to achieve several of the required characteristics. Afterwards, the merger techniques of multiple MPPTs and the combination of both (single and multiple MPPTs’ techniques) due to integrate the benefits of each algorithm while removing their limitations. Comparing and surveying MPPT algorithms in general took a significant amount of time. Despite this, there is a limited literature examining the combination techniques towards the multiple MPPT techniques and the single one. This paper presents the work and uses MATLAB/Simulink platform to simulate it. It is based on a study that contrasts single MPPT techniques with different combinations, namely the constant voltage (CV) method, the perturb and observe (P&O) method and the combination of both (CV+P&O), in order to validate MPPT's better performance.

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Hichem, Louki, Merabet Leila, and Omeiri Amar. "The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 1453–64. http://dx.doi.org/10.11591/eei.v13i3.6416.

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Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

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Muldi, Yuhendri, Muskhir Mukhlidi, and Taali. "A novel optimum tip speed ratio control of low speed wind turbine generator based on type-2 fuzzy system." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1189–97. https://doi.org/10.11591/eei.v8i4.1450.

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Variable speed control of wind turbine generator systems have been developed to get maximum output power at every wind speed variation, also called Maximum Power Points Tracking (MPPT). Generally, MPPT control system consists of MPPT algorithm to track the controller reference and generator speed controller. In this paper, MPPT control system is proposed for low speed wind turbine generator systems (WTGs) with MPPT algorithms based on optimum tip speed ratio (TSR) and generator speed controller based on field oriented control using type-2 fuzzy system (T2FS). The WTGs are designed using horizontal axis wind turbines to drive permanent magnet synchronous generators (PMSG). The simulation show that the MPPT system based optimum TSR has been able to control the generator output power around the maximum point at all wind speeds.

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Contreras Carmona, Itzel, Belem Saldivar, Otniel Portillo-Rodríguez, Víctor Manuel Ramírez Rivera, Leopoldo Gil Antonio, and Juan Manuel Jacinto-Villegas. "A novel strategy for the MPPT in a photovoltaic system via sliding modes control." PLOS ONE 19, no. 12 (2024): e0311831. https://doi.org/10.1371/journal.pone.0311831.

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This paper proposes a robust maximum power point tracking algorithm based on a super twisting sliding modes controller. The underlying idea is solving the classical trajectory tracking control problem where the maximum power point defines the reference path. This trajectory is determined through two approaches: a) using the simplest linear and multiple regression models that can be constructed from the solar irradiance and temperature, and b) considering optimum operating parameters derived from the photovoltaic system’s characteristics. The proposal is compared with the classical methods Perturbation and Observation and Incremental Conductance, as well as with two recently reported hybrid algorithm based on Artificial Neural Networks: one uses the Levenberg-Marquardt algorithm and the other applies Bayesian regularization to generate current and voltage references, respectively. Both use a Proportional-Integral-Derivative controller to solve the maximum power point tracking problem. Numerical simulations confirm the effectiveness of the method proposed in this work regarding convergence time, power efficiency, and amplitude of oscillations. Furthermore, it has been shown that, although no significant differences in the system response are observed with respect to the Artificial Neural Networks-based methods, the proposed algorithm with a reference generated through a linear regression constitutes a low-complexity solution that does not require a temperature sensor to efficiently solve the maximum power point tracking problem.

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Masry, Mohamed Zaghloul-El, Abdallah Mohammed, Fathy Amer, and Roaa Mubarak. "New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems." Sustainability 15, no. 14 (2023): 10884. http://dx.doi.org/10.3390/su151410884.

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This research aimed to increase the power captured from photovoltaic (PV) systems by continuously adjusting the PV systems to work at the maximum power point under climate changes such as solar irradiance change and temperature change and by tracking the global maximum power under partial shading conditions (PSCs). Under the effect of partial shading (PS), the PV curve has many local maximum peaks (LMPs) and one global maximum peak (GMP) which is dynamic because it changes with time when the shading pattern (SP) changes. The traditional maximum power point tracking (MPPT) methods are unable to track the Dynamic GMP and may fall into one of the LMPs. Many modern MPPT methods have been introduced that can track the Dynamic GMP, but their effectiveness can be improved. In this respect, this work introduces a new optimal MPPT technique to enhance the performance of the maximum power point tracking of solar cells under environmental changes and partial shading conditions. The proposed technique combines three well-known and important MPPT techniques, which are the Artificial Neural Network (ANN), Variable Step Perturb and Observe (VSP&O), and Fuzzy Logic Controller (FLC). Artificial Neural Network gives a voltage near the optimum voltage, Variable Step Perturb and Observe updates the voltage to get close to the optimum voltage, and Fuzzy Logic Controller updates the step size of the (P&O) technique. The proposed hybrid ANN-VSP&O-FLC technique showed its ability to track the Dynamic GMP accurately and quickly under the variation in the shading patterns with time and its ability to follow maximum power efficiently and quickly under climate changes. The proposed hybrid ANN-VSP&O-FLC technique also showed very low distortions in waveforms and very low oscillations around the steady state. The proposed hybrid ANN-VSP&O-FLC technique was compared to the most recent and effective MPPT techniques in terms of steady-state behavior, tracking speed, tracking efficiency, and distortions in waveforms, and the comparison showed that it is superior to them, with lower distortions in waveforms, a faster tracking speed (less than 0.1 s), higher tracking efficiency (greater than 99.65%), and lower oscillations around the steady state (less than 2 Watts).

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Junaid Khan, Mohammad, Md Naqui Akhtar, Mashhood Hasan, Hasmit Malik, Md Fahim Ansari, and Asyraf Afthanorhan. "ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions." International Journal of Mathematical, Engineering and Management Sciences 9, no. 5 (2024): 1106–23. http://dx.doi.org/10.33889/ijmems.2024.9.5.058.

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Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

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Islam, F. R., K. Prakash, K. A. Mamun, A. Lallu, and R. Mudliar. "Design a Optimum MPPT Controller for Solar Energy System." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (2016): 545. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp545-553.

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Solar energy is compared to be the best potential source of renewable energy in Pacific region. For this reason a photovoltaic cell is needed to harvest this kind of energy, gathering the most of it and the PV having a good efficiency. The maximum efficiency is achieved when the PV works at its Maximum Power Point which entirely depends on the irradiation and temperature. This paper proposes a new design of hybrid Maximum Power Point Tracking and a comparative study is made with various existing MPPT techniques which include Perturb and Observe method, Incremental Conductance and Fuzzy Logic. From the comprehensive comparison study between existing MPPT technique and the proposed MPPT technique/theory, a hardware setup was demonstrated to verify the proposed design by charge controller in photovoltaic systems to which maximize the output power under various lighting conditions. The design is based on the computed results using the buck-boost DC-DC conveter. From the simulation, the proposed method tends to show better performance with almost no oscillations around the MPP.

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S.A., Khan, Mahmood T., and Awan K.S. "A nature based novel maximum power point tracking algorithm for partial shading conditions." Electrical Engineering & Electromechanics, no. 6 (December 3, 2021): 54–63. https://doi.org/10.20998/2074-272X.2021.6.08.

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Introduction. The huge demand of green energy over past few decades have drawn the interest of scientists and researchers. Solar energy is the most abundant and easily available source but there have been so many problems with its optimum extraction of output. The factors affecting the maximum power point tracking of PV systems are input irradiance, temperature, load etc. The variations in irradiance level lead to partial shading that causes reduction in performance by not letting system to operate at maximum power point. Many methods have been proposed in literature to optimize the performance of PV systems but each method has shortcomings that have failed all of them. The actual problem occurs when partial shading is very strong; this is where most of the methods totally fail. So proposed work addresses this issue and solves it to the fullest. The novelty in the proposed work is that it introduces a new nature-based algorithm that works on the principle of plant propagation. It is a natural optimization technique that plants follow to survive and propagate in different environmental conditions. The proposed method efficiently tracks the global peak under all shading conditions and is simple to implement with high accuracy and tracking speed. Purpose. Building an algorithm that can track global peak of photovoltaic systems under all shading conditions and extracts the maximum possible power from the system, and is simple and easy to implement. Methods. The method is implemented in MATLAB / Simulink on an electrical model that uses a PV array model. Different shadings are applied to check for the results. Results. The results have shown that for different photovoltaic configurations the algorithm performs very good under uniform and partial shadings conditions. Its accuracy, tracking efficiency and tracking time has increased reasonably. Practical value. The project can be very beneficial to people as it enhances the performances of PV systems that can make them self-sufficient in electrical energy, focuses on sustainable development and reduces pollution. This way it can have huge impact on human life.

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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&amp;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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Nagaraju, Dr V. Siva. "PID CONTROLLER DESIGN FOR SOLAR TRACKING SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41045.

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The aim of this research is to design solar tracking in order to orientate the solar panel to the maximum radiation at all times. In the present work, designing of an optimum proportional-integral-derivative (PID) controller is used to control a dual axis solar tracker system, namely: rotation and elevation. To obtain the optimum result, two kinds of testing were conducted, namely, testing of mechanical design and PID parameters. As a result, the mechanical testing indicated that this system can work properly based on the parameters input. Similar to the PID testing, the response of PID set point with set position rotation and elevation according to the parameters input. In addition, this research was conducted in an electrical power system Laboratory in the State Polytechnic of Ujung Pandang, Makassar, Indonesia. Key Words: Control; design; PID ; Solar; Tracking

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Charin, Chanuri, Dahaman Ishak, Muhammad Ammirrul Atiqi Mohd Zainuri, and Baharuddin Ismail. "Modified Levy Flight Optimization for a Maximum Power Point Tracking Algorithm under Partial Shading." Applied Sciences 11, no. 3 (2021): 992. http://dx.doi.org/10.3390/app11030992.

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This paper presents a novel modified Levy flight optimization for a photovoltaic PV solar energy system. Conventionally, the Perturb and Observe (P&O) algorithm has been widely deployed in most applications due to its simplicity and ease of implementation. However, P&O suffers from steady-state oscillation and stability, besides its failure in tracking the optimum power under partial shading conditions and fast irradiance changes. Therefore, a modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods. The proposed modified Levy flight optimization is verified with simulations and experiments under uniform, non-uniform, and dynamic conditions. All results prove the advantages of the proposed modified Levy flight optimization in extracting the optimal power with a fast response and high efficiency from the PV arrays.

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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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Charin, Chanuri, Dahaman Ishak, Muhammad Ammirrul Atiqi Mohd Zainuri, Baharuddin Ismail Turki Alsuwian, and Adam R. H. Alhawari. "Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking." Energies 15, no. 19 (2022): 7370. http://dx.doi.org/10.3390/en15197370.

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This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

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Hameed, Waleed I., Ameer L. Saleh, Baha A. Sawadi, Yasir I. A. Al-Yasir, and Raed A. Abd-Alhameed. "Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network." Inventions 4, no. 3 (2019): 33. http://dx.doi.org/10.3390/inventions4030033.

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The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel and produces an optimal value of duty ration for the boost converter to obtain the MPPT. The coefficients of this controller have been refined based upon previous data sets using the irradiance and temperature. A gradient descent algorithm is employed to improve the parameters of the FNN controller to achieve an optimal response. The validity of the PV system using the MPPT technique based on the FNN controller is further demonstrated via a series of experimental tests at different ambient conditions. The simulation results show how the MPPT technique based on the FNN controller is more effective in maintaining the optimal power values compared with conventional techniques.

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Rathore, Gajendra Singh, B. Gopal Krishna, R. N. Patel, and Sanjay Tiwari. "Various Techniques of MPPT Based Charge Controller and Comparison of A/C with D/C Home Appliances - A Review." Journal of Ravishankar University (PART-B) 34, no. 1 (2021): 87–95. http://dx.doi.org/10.52228/jrub.2021-34-1-13.

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Controlling the PV array to generate the maximum power at certain environment conditions, the efficiency of the PV generation system could be improved. Using control algorithms, the PV array can operate at the maximum power point. This self-optimization process is referred as Maximum power point tracking (MPPT). A maximum power point tracker (MPPT) is a power electronic DC-DC converter inserted between the PV array and its load to achieve optimum matching. Researchers have studied and developed numerous methods and used many algorithms to track the Maximum power point of PV Module and extract maximum power using MPPT technique. This review article accumulates various algorithms through which MPPT could be attained and assists the researchers to understand the principle of their working. The paper also gives an idea to about less explored DC appliances and their viabilities in existing and proposed DC system.

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Khalessi, N., M. Niroomand, J. Dadkhah, and S. Y. Nikouei. "A Firework-Based GMPPT with Variable Sampling Time for PV Systems." Mathematical Problems in Engineering 2020 (November 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/6130202.

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Photovoltaic systems have a nonlinear characteristic in which there is one optimum operating point called Maximum Power Point (MPP). However, when PV panels are partially shaded by surrounding objects, there are several MPPs, of which one of them is Global MPP (GMPP). Therefore, conventional Maximum Power Point Tracking (MPPT) algorithms get trapped into local MPPs. As a result, a multitude of Global MPPT (GMPPT) algorithms have been proposed. An outstanding GMPPT algorithm as well as the fast-tracking speed should find GMPP in complicated shading patterns where not only there are lots of MPPs, but also the peaks are close together. Therefore, in this paper, a novel GMPPT based on firework algorithm is proposed which is able to find GMPP in complicated shading patterns with fast tracking speed. Moreover, the firework is combined with Perturb-and-Observe (P&O) algorithm to reduce the computational effort in a way that the firework is only used to recognize GMPP; afterwards, P&O algorithm completes the tracking. Furthermore, the variable sampling time technique, based on the system settling time, speeds up the tracking process considerably. Finally, the proposed method is compared with previous works, simulated, and implemented on an experimental setup to prove its superiority.

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Kim, Jungmoon, Jihwan Kim, and Chulwoo Kim. "A Regulated Charge Pump With a Low-Power Integrated Optimum Power Point Tracking Algorithm for Indoor Solar Energy Harvesting." IEEE Transactions on Circuits and Systems II: Express Briefs 58, no. 12 (2011): 802–6. http://dx.doi.org/10.1109/tcsii.2011.2173971.

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Thi, Thom Hoang, and Huong Le Thi. "Application of mutant particle swarm optimization for MPPT in photovoltaic system." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 2 (2020): 600–607. https://doi.org/10.11591/ijeecs.v19.i2.pp600-607.

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The P –V characteristic of a photovoltaic system (PVs) is non-linear and de-pends entirely on the extreme environmental condition, thus a large amount PV energy is lost in the environment. To enhance the operating efficiency of the PVs, a maximum power point tracking (MPPT) controller is normally equipped in the system. This paper proposes a new mutant particle swarm optimization (MPSO) algorithm for tracking the maximum power point (MPP) in the PVs. The MPSO-based MPPT algorithm not only surmounts the steady-state oscillation (SSO) around the MPP, but also tracks accurately the optimum power under different varying environmental conditions. To demonstrate the effectiveness of the proposed method, MATLAB simulations are implemented in three challenging scenarios to the PV system, including changing irradiation, load variation and partial shading condition (PSC). Furthermore, the obtained results are compared to some of the con-ventional MPPT algorithms, such as incremental conductance (INC) and clas-sical particle swarm optimization (PSO) in order to show the superiority of the proposed approach in improving the efficiency of PVs.

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AlRasheed, Khaled S., Siti Fauziah Toha, Hazleen Anuar, and Yose Fachmi Buys. "Maximum Power Point Tracking using Light Dependent Resistor and DC motor for Solar Photovoltaic System in Kuwait." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 222–28. http://dx.doi.org/10.35940/ijrte.e5272.019521.

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In this paper a Maximum Power point (MPP) tracking system is developed using dual-axis DC motor feedback tracking control system. An efficient and accurate DC motor system is used to increase the system efficiency and reduces the solar cell system coast. The suggested automated DC motor control system based on the photovoltaic ( PV ) modules operated with the μ-microcontroller. This servo system will track the sun rays in order to get MPP during the day using direct radiation. A photometric cell is used to sensor the direct sun radiation and to feed a signal to the μ microcontroller and then select the DC motor mechanism to deliver optimum energy. The proposed system is demonstrated through simulation results. Finally, using the proposed system based on microcontroller, the system will be more efficient, minimum cost, and maximum power transfer is obtained.

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Khaled, S. AlRasheed, Fauziah Toha Siti, Anuar Hazleen, and Fachmi Buys Yose. "Maximum Power Point Tracking using Light Dependent Resistor and DC motor for Solar Photovoltaic System in Kuwait." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 222–28. https://doi.org/10.35940/ijrte.E5272.019521.

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Abstract: In this paper a Maximum Power point (MPP) tracking system is developed using dual-axis DC motor feedback tracking control system. An efficient and accurate DC motor system is used to increase the system efficiency and reduces the solar cell system coast. The suggested automated DC motor control system based on the photovoltaic ( PV ) modules operated with the μ-microcontroller. This servo system will track the sun rays in order to get MPP during the day using direct radiation. A photometric cell is used to sensor the direct sun radiation and to feed a signal to the μ microcontroller and then select the DC motor mechanism to deliver optimum energy. The proposed system is demonstrated through simulation results. Finally, using the proposed system based on microcontroller, the system will be more efficient, minimum cost, and maximum power transfer is obtained.

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Luo, Wenxiang, Yong Huang, Wenhao Zhou, Yin Nie, and Chunqin Lai. "MPPT Control Research of Improved Gray Wolf Algorithm According to Levy Flight and Greedy Strategy." Journal of Physics: Conference Series 2456, no. 1 (2023): 012032. http://dx.doi.org/10.1088/1742-6596/2456/1/012032.

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Abstract MPPT control study of improved Gray Wolf algorithm (GWO) according to Levy flight and greedy strategy: Solving the problems of maximum power point tracking (MPPT) with traditional Gray Wolf algorithm (GWO) under local shadows of photovoltaic arrays and sudden environmental changes, which is easily trapped in local optimum and has a slow convergence rate, and poor solution accuracy, a improved gray wolf algorithm (LGWO) according to Levy flight and greedy strategy is presented. It is used for the first time for maximum power point tracking under partial shadow and dynamic shadow changes of photovoltaic array. It is based on the traditional Grey Wolf algorithm (GWO), it introduces Levy’s flight search strategy, improve the algorithm of global search ability, expands search range, and filters optimal range through greedy strategy to further accelerate the convergence speed. MPPT simulation model according to Boost circuit is built using MATLAB/Simulink to verify. Experiments in the presence of partial shadows and shadow mutations. The results of simulation experiments show that the ameliorated Gray Wolf algorithm (LGWO) improves the tracking accuracy of MPPT by 0.03%, improves the convergence speed by 1.1 times and is more stable after reaching the maximum number of iterations. This verifies the feasibility and superiority of the ameliorated Grey Wolf algorithm in maximum power point tracking control.

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Yadav, Indresh, Sanjay Kumar Maurya, and Gaurav Kumar Gupta. "A Literature review on industrially accepted MPPT techniques for solar PV system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 2117. http://dx.doi.org/10.11591/ijece.v10i2.pp2117-2127.

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Solar energy is a clean renewable energy and it is available around 89,000 TW on the earth surface. To get maximum power from a solar PV system with minimum power transfer loss is one of the main design objectives of an energy transferring network. Power electronic devices perform a very important character for an efficient PV power tracking system control and either incorporates to transfer the generated power to the ac/dc grid or battery storage system. In this case the duty of the power electronics devices used in PV system is to track maximum power point under different operating conditions of environment, so that power tracking efficiency of solar PV system can be improved. This paper encapsulates based the on performance comparisions on the behavior of MPP under uniform and nonuniform operating conditions and selects the optimum duty cycle for industrially accepted MPPT techniques with their algorithm.

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Indresh, Yadav, Kumar Maurya Sanjay, and Kumar Gupta Gaurav. "A literature review on industrially accepted MPPT techniques for solar PV system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 2117–27. https://doi.org/10.11591/ijece.v10i2.pp2117-2127.

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Solar energy is a clean renewable energy and it is available around 89,000 TW on the earth surface. To get maximum power from a solar PV system with minimum power transfer loss is one of the main design objectives of an energy transferring network. Power electronic devices perform a very important character for an efficient PV power tracking system control and either incorporates to transfer the generated power to the ac/dc grid or battery storage system. In this case the duty of the power electronics devices used in PV system is to track maximum power point under different operating conditions of environment, so that power tracking efficiency of solar PV system can be improved. This paper encapsulates based the on performance comparisions on the behavior of MPP under uniform and nonuniform operating conditions and selects the optimum duty cycle for industrially accepted MPPT techniques with their algorithm.

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Zhang, Xiao Lian, Ming Hui Yin, Lian Jun Zhou, and Yun Zou. "Research on the Compensation Coefficients of the Improved MPPT Control Based on Reduction of Tracking Range." Advanced Materials Research 724-725 (August 2013): 598–604. http://dx.doi.org/10.4028/www.scientific.net/amr.724-725.598.

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The improved maximum power point tracking (MPPT) control based on the reduction of tracking range could reduce the tracking distance of wind turbine effectively, and improve the tracking efficiency. In this study, the relationship between the compensation coefficient of the improved MPPT control and several factors was investigated. Based on simulations of the simplified wind turbine model, wind conditions, air density and wind turbine parameters were used in the studies to investigate the relationship between the optimum compensation coefficients and these corresponding conditions. It can be indicated that in the fact, compensation coefficient is majorly determined by the tracking targets of MPPT and the dynamic performance of wind turbines that determined by the conditions mentioned above. The results also provide guidance for further studies in this area.

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Arunprasath Thanabalan, Chanuri Charin, Baharuddin Ismail, Fatin Nadia Azman Fauzi, and Azirah Baharum. "Performance Analysis of Deterministic Particle Swarm Optimization MPPT for a Standalone Photovoltaic System." Journal of Advanced Research in Applied Sciences and Engineering Technology 49, no. 1 (2024): 108–16. http://dx.doi.org/10.37934/araset.49.1.108116.

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This paper presents a study of Deterministic Particle Swarm Optimization (DPSO) in maximum power point tracking. DPSO is applied to a standalone PV system with a boost converter. This method is implemented primary to solve the problem in conventional techniques such as Perturb and Observe (P&O) and Incremental Conductance (IC) in failure to detect optimum point under certain condition, fixed step-size and high steady-state oscillation. Deterministic method is applied to conventional Particle Swarm Optimization (PSO) and takes advantage over guiding the behaviour of the particles through experience. The velocity of the particle is predicted and evaluated until the optimum point is achieved. A standalone photovoltaic (PV) system is constructed with MATLAB Simulink and DPSO is deployed and tested by simulation. The effectiveness of DPSO is evaluated under uniform condition at standard test condition (STC), medium and low irradiance. The results show that the DPSO successfully converge at optimum point with low steady-state oscillation, and it has high efficiency.

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Chang, En-Chih, Chun-An Cheng, and Rong-Ching Wu. "Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities." Wireless Communications and Mobile Computing 2022 (April 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/4213217.

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The combination of artificial intelligence of things (AIoT) and photovoltaic power generation can save energy and reduce carbon emissions and further promote the development of smart cities. In order to obtain the maximum power output from photovoltaic (PV) arrays, we can use optimal maximum power point tracking (MPPT) technique with AIoT sensing to improve system efficiency. The optimal MPPT technique is the finite-time terminal attractor (FTTA) based on the gradient particle swarm optimization (GPSO), which can be applied to track the maximum power of a PV array system. The FTTA not only provides fast finite-time convergence but also attenuates steady-state errors, making it ideal for nonlinear system applications. The GPSO is used to search the control parameters of the FTTA, which is able to find the global best solution. This avoids unmodeled dynamic behavior of the system excited by the quiver, which slows down the control convergence and prematurely traps the system into a local optimum. The MATLAB computer software is used to simulate the proposed PV maximum power point tracking system. The results show that more accurate and better tracking control of the PV array can be produced under partial shading conditions and then improve the steady-state and transient performance.

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Alhamdawee, Ehsan Mohsin, Nashiren Farzilah Binti Mailah, Mohd Amran Mohd Radzi, Suhaidi Bin Shafie, Shahrooz Hajighorbani, and Ahmed Qasim Turki. "Comparison of developed FLC and P&O MPPT algorithms for improving PV system performance at variable irradiance conditions." World Journal of Engineering 13, no. 6 (2016): 494–99. http://dx.doi.org/10.1108/wje-09-2016-0082.

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Purpose This work aims to overcome the drawbacks of the nonlinear characteristics of the photo-voltaic (PV) system which are affected by the atmospheric variations. Design/methodology/approach As a result, the optimum power point on these characteristics accordingly changes and the efficiency of photovoltaic systems reduces. Maximum power point tracking (MPPT) algorithms track this optimum point and enhance the efficiency despite the irradiance and temperature changes. Findings The conventional perturbation and observation (P&O) algorithm uses fixed step sizes to increment and decrement the duty ratio that leads to slow response time and continuous oscillation around the MPP at steady state conditions. The paper proposes a fuzzy logic-based controller that overcomes the drawbacks of P&O algorithm in term of response time and the oscillation. Originality/value MATLAB/Simulink environment was used to model and simulate the KC200GT PV module, direct current (DC)-DC boost converter and the MPPT algorithms.

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Figueiroa, Vasco, and João Paulo N. Torres. "Simulation of a Small Smart Greenhouse." Designs 6, no. 6 (2022): 106. http://dx.doi.org/10.3390/designs6060106.

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This paper investigates the design and implementation of a small greenhouse, based on an estimation of the required annual electrical loads, using robust energy modelling free software, namely OpenStudio. The greenhouse optimum materials, shape and orientation were estimated from this software, using weather file data and established environmental set points. Real-world electrical load estimations for the temperature, irrigation and lighting subsystems were consequently made, resulting in a good estimation of the required solar panel and battery combination. Sensors and actuators to physically establish the environmental set points were described, controlling with a microcontroller, while minimizing power losses. To maximize power throughput to the battery, a maximum power point tracking algorithm was described and modelled in Simulink, specifically for this system, using the microcontroller to implement a Perturb and Observe algorithm.

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Vijay Muni, T., K. S. Srikanth, N. Venkatesh, and K. L. Sumedha. "A high performance hybrid MPPT control scheme for a grid connected PV system based three level NPCMLI." International Journal of Engineering & Technology 7, no. 2.20 (2018): 37. http://dx.doi.org/10.14419/ijet.v7i2.20.11741.

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This paper discusses about usage of novel control strategy to extract maximum power in case of solar energy conversion system for renewable energy system applications with the power electronic technology based novel 3 level neutral point clamped inverter. Photovoltaic is one of the important renewable energy sources. Compared to other renewal energy sources photovoltaic energy is clean and abundantly available. Solar power is considered a very promising source for electric power generation. It is generally seen that the renewable energy system is highly stochastic in nature and does not guarantee continuous power throughout the period. The solar based renewable energy resource has to be coupled with the existing conventional gird which might overcome the stochastic behavior of the non conventional resources. To meet the demand of the various types of customers, the proposed combination or generation mix is highly desirable and ray of hope for future generation. The efficient usage of solar based power generation is certainly possible when interfaced with the existing gird and meet the load requirement of diversified customers who are dependent on electric power.Low efficiency of the solar PV module leads to research and improvement about control technology of different sub-modules of solar based renewable energy generation system interfaced with the existing grid. Generally, the photovoltaic system rating is constrained by the efficiency of percentage output power obtained from the PV panel. In order to achieve this objective , controller to extract the maximum power from solar panel is designed and incorporated in the system known as Maximum power point tracking (MPPT). The return on investment is feasible with various constraints like environmental factors, other internal and external factors, only when the power output is optimum and maximum with the existing operating conditions.The maximum energy production that is power should be extracted from the solar panel in the given conditions. This process is called maximum powerpoint tracking. The point at each the proposed aim is reached, is called point of maximum power. The efficiency of the entire photovoltaic energy generation depends on the operating characteristic point. The load should ultimately get the optimum possible power obtained from the photovoltaic generation at the operating characteristic point [5]. Therefore, there should be some control logic or control technique in form of a suitable controller which is designated as maximum power point tracking (MPPT) controller. This controller is designed in such a way that the maximum power is obtained from the photovoltaic module.In this work, Direct Prediction Method and P&O is implemented as a hybrid MPPT control scheme grid connected PV system based NPCMLI. MLI helpful in increasing dynamic performance of the PV system with the existing operating conditions. This MLI offers less harmonic disturbances and goes near to maximum possible power factor operation i.e. 1. The results are verified in Matlab /Simulink environment.

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Mr., Sukhdeo R. Patankar, and Pratik Ghutke Prof. "Optimizing Power Ramp Capabilities of PV Systems using Fuzzy MPPT." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 495–500. https://doi.org/10.31142/ijtsrd23759.

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Maximal Power Point Tracking MPPT algorithms are generally sufficient for tracking the optimum power point of a solar or photo voltaic PV system. But when ramps occur in the PV systems, then only MPPT controllers are not sufficient and we need an improved controller to perform the task of ramp minimization. In this paper we propose a fuzzy based MPPT controller to control the ramp capabilities of a PV system, which is found to be superior to the normal MPPT based controller. The results and observations show that the proposed controller has 10 higher efficiency in ramp control when compared to standard MPPT controller, and thus it can be used in real time environments where there is a need to control the ramp capabilities to a large extent. Mr. Sukhdeo R. Patankar | Prof. Pratik Ghutke "Optimizing Power Ramp Capabilities of PV Systems using Fuzzy MPPT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23759.pdf

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Journal articles on the topic 'Optimum power point tracking'

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