An Efficient Maximum Power Point Tracking Controller for Photovoltaic Systems Using Takagi–Sugeno Fuzzy Models

Ounnas, Djamel; Ramdani, Messaoud; Chenikher, Salah; Bouktir, Tarek

doi:10.1007/s13369-017-2532-0

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…The proposed ANFIS method generated change in the duty cycle based on a historical change of PV power and derivate in this value. In [34], authors designed an efficient MPPT technique based ANFIS for PV systems to determine the MPP under different weather conditions. The G and T were selected as the inputs of the ANFIS model, whereas the optimal voltage at the MPP was the output.…”

Section: Related Workmentioning

confidence: 99%

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

2019

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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%.

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Section: Related Workmentioning

confidence: 99%

“…The results in [24][25][26][27][28][29][30][31][32][33][34] reported that the conventional ANFIS-MPPT based on theatrical data increased tracking speed and reduced oscillations. However, they were not achieving a higher efficiency when compared with hybrid methods because of a shortage of accurate training data.…”

Section: Related Workmentioning

confidence: 99%

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

2019

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“…In general, a parallel distributed compensation (PDC) scheme is used to construct a global fuzzy controller by blending all local state feedback controllers. The Lyapunov method is usually employed to find stability conditions of the system, which can be transformed into linear matrix inequalities (LMIs) and solved easily using convex optimization techniques [17]. In the literature, many works have been devoted to the stabilization problem of nonlinear systems based on T-S fuzzy models whereas the problem of tracking control has been poorly treated.…”

Section: Introductionmentioning

confidence: 99%

Real-time Implementation of a Takagi-Sugeno Fuzzy Controller for a DC-DC Boost Converter

Daouadi¹,

Ounass²,

Soufi³

et al. 2020

EEA

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“…In the recent year, decreasing the fossil fuel consumption and detraction harmful emission become the important topics for many researchers to reduce the effects of climate change [1]. To achieve this task, most of the researchers in this field trend using the renewable energy such as: wind energy [2][3][4], solar energy [5][6][7][8], wave energy [9][10] etc. instead of the fossil fuels to protect the environment.…”

Section: Introductionmentioning

confidence: 99%

Design of pitch angle controller for wind turbine based on pi neurofuzzy model

Aldair

Rashid

Halihal³

et al. 2019

IJEECS

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<p>Aerodynamic torque of wind turbine is adjusted by controlling the pitch angle of the blades of the turbine when the wind speed is higher than rated wind speed. So that, in the recent research in this field, the pitch angle controller becomes dominated controller type for extracting the electrical power from the wind energy. Three types of the pitch angle control systems are designed to construct the speed controller: conventional PI controller, Neurofuzzy controller and modified PI-Neurofuzzy controller. The results are shown that the modified PI-Neurofuzzy controller is more efficient than the others because the rotation speed of generator is kept almost constant. It means that the generated output power has remained constant at maximum power limited even the wind speed rises up the rated wind speed.</p>

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An Efficient Maximum Power Point Tracking Controller for Photovoltaic Systems Using Takagi–Sugeno Fuzzy Models

Cited by 26 publications

References 28 publications

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

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

Real-time Implementation of a Takagi-Sugeno Fuzzy Controller for a DC-DC Boost Converter

Design of pitch angle controller for wind turbine based on pi neurofuzzy model

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