scholarly journals Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 858 ◽  
Author(s):  
Sadeq D. Al-Majidi ◽  
Maysam F. Abbod ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Training Data ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Inference System ◽  
Photovoltaic Array ◽  
Power Point

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

scholarly journals Simulation and Implementation of a Modified ANFIS MPPT Technique

2020 ◽  
Author(s):  
Bachar Meryem ◽  
Naddami Ahmed ◽  
Fahli Ahmed
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Optimal Operation ◽  
Climatic Conditions ◽  
Buck Converter ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Inference System ◽  
Power Point

The maximum power point tracking (MPPT) algorithms ensure optimal operation of a photovoltaic (PV) system to extract the maximum PV power, regardless of the climatic conditions. This paper exposes the study, design, simulation and implementation of a modified advanced neural fuzzy inference system (ANFIS) MPPT algorithm based on fuzzy data for a PV system. The studied system includes a PV array, a DC/DC buck converter, the ANFIS controller, a proportional-integral (PI) controller, and a load. The simulation and experimental tests are carried out with the MATLAB/Simulink software and LabVIEW, respectively. Moreover, the obtained results are compared with previously published results by incremental conductance (IC) and fuzzy logic (FL) algorithms under different climatic conditions of irradiation and temperature. The results show that the proposed ANFIS algorithm is able to track the maximum power point for varying climatic conditions. Furthermore, the comparison analysis reveals that the PV system using ANFIS algorithm has more efficient and better dynamic response than FL and IC.

Adaptive Neuro-Fuzzy Inference System-based Improvement of Perturb and Observe Maximum Power Point Tracking Method for Photovoltaic Systems

2017 ◽  
Vol 8 (3) ◽  
pp. 1327 ◽  
Author(s):  
Khaled Bataineh ◽  
Yazan Taamneh
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Inference System ◽  
Tracking Method ◽  
Perturb And Observe ◽  
Power Point

This paper presents a maximum power point (MPP) tracking method based on a hybrid combination between the fuzzy logic controller (FLC) and the conventional Perturb-and-Observe (P&O) method. The proposed algorithm utilizes the FLC to initialize P&O algorithm with an initial duty cycle.  MATLAB/Simulink models consisting of, the photovoltaic system, boost converter and controllers, are built to evaluate the performance of the proposed algorithm. To accurately illustrate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is investigated difficult weather conditions including sudden changes and partial shading. The results showed that the proposed algorithm successfully reaches MPP in all scenarios.

Optimal Command for Photovoltaic Systems in Real Outdoor Weather Conditions

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Hafsa Abouadane ◽  
Abderrahim Fakkar ◽  
Benyounes Oukarfi
Keyword(s):  
Maximum Power Point Tracking ◽  
Accurate Determination ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

The photovoltaic panel is characterized by a unique point called the maximum power point (MPP) where the panel produces its maximum power. However, this point is highly influenced by the weather conditions and the fluctuation of load which drop the efficiency of the photovoltaic system. Therefore, the insertion of the maximum power point tracking (MPPT) is compulsory to track the maximum power of the panel. The approach adopted in this paper is based on combining the strengths of two maximum power point tracking techniques. As a result, an efficient maximum power point tracking method is obtained. It leads to an accurate determination of the MPP during different situations of climatic conditions and load. To validate the effectiveness of the proposed MPPT method, it has been simulated in matlab/simulink under different conditions.

Research on Maximum Power Point Tracking Method for Photovoltaic System

2012 ◽  
Vol 220-223 ◽  
pp. 2091-2094
Author(s):  
Yue Shen Lai ◽  
Sheng Dong Hou ◽  
Gang Wang
Keyword(s):  
Maximum Power Point Tracking ◽  
Mathematic Model ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Computer Simulation Model ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Photovoltaic Array is nonlinear,and the power generated by it is influenced by sun light,temperature,load and so on In order to improve the system efficiency, firstly, analyzed the physical and mathematic model of photovoltaic array, through the MATLAB Simulink application software simulation tools, set up a computer simulation model of photovoltaic modules.Secondly,this paper gives the improved Perturbation and Observation Method and the Boost circuit used in the controller.after the analysis of some common maximum power point tracking algorithms and DC-DC circuits. Experiments shows that the controller method can rapidly to track the maximum power point,and increase the cell efficiency.

Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System

2015 ◽  
Vol 785 ◽  
pp. 215-219
Author(s):  
Ammar Hussein Mutlag ◽  
Hussein Shareef ◽  
Azah Mohamed ◽  
Jamal Abd Ali ◽  
Maytham S. Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Inference System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neuro Fuzzy ◽  
Power Point

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.

scholarly journals Application and Comparison Between the Conventional Methods and PSO Method for Maximum Power Point Extraction in Photovoltaic Systems Under Partial Shading Conditions

2018 ◽  
Vol 9 (2) ◽  
pp. 631 ◽  
Author(s):  
Bennis Ghita ◽  
Karim Mohammed ◽  
Lagrioui Ahmed
Keyword(s):  
Fuzzy Control ◽  
Pso Algorithm ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Steady State Oscillation ◽  
Power Point

Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.

Optimization of Photovoltaic Array Configurations in Photovoltaic System

2014 ◽  
Vol 1070-1072 ◽  
pp. 48-51
Author(s):  
Wen Ting Jia ◽  
Xue Ye Wei ◽  
Jun Hong Zhang ◽  
Yi Fei Meng
Keyword(s):  
Output Power ◽  
Photovoltaic Cell ◽  
Energy Conversion Efficiency ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Pv System ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Pv Array ◽  
Power Point

Closely related to the actual output power and the light intensity, the temperature of the photovoltaic cell panel and the load of the PV array or the like. In the case of the external environment is stable and load conditions change, the output power of the PV modules exist Maximum Power Point, in order to improve the self-tracking PV system energy conversion efficiency, maximum power point tracking method may ensure the system running at maximum power points. Photovoltaic power generation system, optimize allocation method of PV array are also discussed in this paper.

scholarly journals Modeling of photovoltaic system with maximum power point tracking control by neural networks

2019 ◽  
Vol 10 (3) ◽  
pp. 1575
Author(s):  
Farid Saadaoui ◽  
Khaled Mammar ◽  
Abdaldjabar Hazzab
Keyword(s):  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Photovoltaic Module ◽  
Neuron Network ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
And Control

<p>This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic  module inputs (temperature and  solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours.</p><p>In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&amp;O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power.</p><p>The entire system is implemented in the Matlab / Simulink environment where simulation results  obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&amp;O.</p>

Sign in / Sign up

Export Citation Format

Share Document