Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit

Chekired, Fathia; Larbes, Cherif; Rekioua, Djamila; Haddad, Firas

doi:10.1016/j.egypro.2011.05.062

Cited by 129 publications

(44 citation statements)

References 11 publications

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“…This method gives fast tracking speed during varying atmospheric conditions. Some methods are based on neural networks and fuzzy logic [3,11,15,16]. The mean advantage is their ability to take into account the nonlinearities without handling nonlinear mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Accurate Maximum Power Point Tracking Algorithm Based on a Photovoltaic Device Model

Bahir

Hassboun

2017

International Journal of Photoenergy

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An accurate method is proposed to track the maximum power point of a photovoltaic module. The method is based on the analytical value of the maximum power point voltage, determined from a mathematical model of the photovoltaic panel. The method has the advantage of accuracy without any oscillations, as with certain conventional methods. The algorithm has also the ability to track accurately the maximum power point under variable atmospheric conditions and load changes. Experimental results are presented to show the effectiveness of the method. The implementation of the method needs an online measurement of irradiance, panel temperature, and panel current and voltage.

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Section: Introductionmentioning

confidence: 99%

Accurate Maximum Power Point Tracking Algorithm Based on a Photovoltaic Device Model

Bahir

Hassboun

2017

International Journal of Photoenergy

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“…The input variables of the fuzzy logic controller differ from one configuration to another. In [11][12][13][14], the input variables to the FLC are the error (E) and the change in error (ΔE). The error can be calculated as the change in the power to the change in the voltage of the PV module ΔP/ΔV The output variable from the FLC is the duty cycle.…”

Section: Introductionmentioning

confidence: 99%

“…The fuzzy logic controller was applied in designing different MPPT controllers [7,8,[10][11][12][13][14][15][16]. They apply a set of linguistic rules to obtain the required duty cycle.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

Noman

Addoweesh

Alolah

2017

International Journal of Photoenergy

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Photovoltaic (PV) module behavior is not linear in nature with respect to environmental conditions and hence exhibits nonlinear PV curves. There is only a single point in the nonlinear PV curve at which the power is maximum. Therefore, special methods have been proposed to track this maximum power point (MPP). This paper proposed an intelligent method for MPP tracking (MPPT) based on adaptive neuro-fuzzy inference system (ANFIS) controller. The proposed system consists of a PV module connected to a DC-DC isolated Ćuk converter and load. A MATLAB/SIMULINK-based MPPT model is built to test the behavior of the proposed method. The proposed method is tested under different weather scenarios. Simulation results exhibit the successful tracking of the proposed method under all ambient conditions. Comparison of the tracking behavior of the proposed method with the perturb and observe method is also presented in the simulation results. In addition, a 220 W prototype with the help of dSPACE 1104 data acquisition system is built and tested under practical weather conditions on a sunny day as well as on a cloudy day. Experimental results are presented to verify the effectiveness of the proposed method. These results exhibit satisfactory performance under different practical weather conditions.

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“…The advantage of INC is it is capable of tracking the MPP more precisely and exhibits less oscillatory behavior around the MPP compared to the P & O method. But the disadvantage is that the results may be unsatisfactory due to its unstable behavior at low insolation levels [9][10].The constant voltage (CV) algorithm is simple but it cannot locate the exact MPP practically but preferred for low levels of insolation [11][12].The feedback voltage or current method employs a feedback control loop but it cannot consider the effect of variations in insolation and temperature [13].In order to track MPPT accurately, neural network is employed but the performance of the PV system is entirely based on how well a neural network has been initially trained [14].To further improve the tracking of PV power, a fuzzy logic controller is reported in the literature which does not require the mathematical model of PV [15]. But the effectiveness of this method depends on user knowledge and skill in choosing the correct rule base table which depends on the chosen membership functions [16].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Adaptive Fuzzy Logic Control Based MPPT for Photovoltaic System

Sankar¹,

Seyezhai²

2016

IJUNESST

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Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit

Cited by 129 publications

References 11 publications

Accurate Maximum Power Point Tracking Algorithm Based on a Photovoltaic Device Model

Accurate Maximum Power Point Tracking Algorithm Based on a Photovoltaic Device Model

Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

Implementation of Adaptive Fuzzy Logic Control Based MPPT for Photovoltaic System

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