Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

Aldair, Ammar A.; Obed, Adel A.; Halihal, Ali F.

doi:10.1016/j.rser.2017.08.071

Cited by 83 publications

(27 citation statements)

References 9 publications

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“…In this work, this task is performed using an AI-type ANFIS algorithm. It is an intelligent method combining FL and ANN [28][29][30][31]. The learning of our controller is done through the retropropagation algorithm, in order to determine the parameters of the premises (adjustment parameters related to the membership functions) and the estimation of the consequent parameters by the least squares method.…”

Section: Command and Supervisionmentioning

confidence: 99%

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Ndiaye

Faye

et al. 2020

International Journal of Photoenergy

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This paper presents a method of intelligent control of a photovoltaic generator (PVG) connected to a load and a battery. The system consists of charging and discharging a battery. An intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) is presented in this work. It performs two separate tasks simultaneously. First, it is used as a PVG Maximum Power Point Tracking (MPPT) command. This same algorithm is used secondly for protecting the battery against deep charges and discharges. A regulation of the DC bus voltage is also carried out by means of a PI corrector for a good supply of the load. The simulation results under MATLAB/Simulink show that the method proposed in this work allows the PV system to function normally by charging and discharging the battery whatever the weather conditions.

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Section: Command and Supervisionmentioning

confidence: 99%

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Ndiaye

Faye

et al. 2020

International Journal of Photoenergy

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“…The data of ANFIS system were collected from characteristics of the PV array under varying weather conditions. In [31], the scholars designed and implemented an ANFIS-MPPT technique using an Field Programmable Gate Array (FPGA) board for standalone photovoltaic systems to demonstrate the usefulness of ANFIS. The solar irradiance and temperature operation were selected as the inputs of the ANFIS model, whilst the optimal current was the output.…”

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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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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“…Adaptive Neuro‐Fuzzy Inference System is an adaptive and trainable multilayer network. The most important advantages of ANFIS in comparison to other conventional systems are fast and accurate learning besides fine tuning of membership functions . The typical structure of ANFIS is shown in Figure , which contains 5 distinct layers.…”

Section: General Overview Of the Systemmentioning

confidence: 99%

Power quality enhancement and power management of a multifunctional interfacing inverter for PV and battery energy storage system

Mousavi

Jalilian

Savaghebi

et al. 2018

Int Trans Electr Energ Syst

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Summary In this paper, the control of an interface inverter for application of a hybrid photovoltaic and battery energy storage system (BESS) is proposed. The control system of the inverter consists of 2 parts including power quality enhancement and active power control. A control approach for battery state of charge and power management is proposed for improving the performance of BESS and load leveling purpose. Furthermore, to prevent the BESS from frequently charge and discharge during a day, the discharge of it can be controlled by operator or microgrid central controller. The power quality enhancement function of the proposed method mitigates the effect of the unbalance and nonlinear loads within the microgrid on the 3‐phase 4‐wire grid. The simulation results show that by using the multifunctional inverters, the unbalance and harmonics of the loads are compensated; furthermore, the neutral current, which is drawn from the grid, is competently compensated. The simulation results show that by using the proposed power management system, the load leveling and charge and discharge management of battery energy storage considering the battery limitation for the sake of extending the battery service life are achieved.

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Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

Cited by 83 publications

References 9 publications

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

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

Power quality enhancement and power management of a multifunctional interfacing inverter for PV and battery energy storage system

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