Adaptive Neuro-Fuzzy Inference System Application for The Identification of a Photovoltaic System and The Forecasting of Its Maximum Power Point

Ndiaye, El Hadji Mbaye; Ndiaye, Ababacar; Tankari, Mahamadou Abdou; Lefebvre, Gill

doi:10.1109/icrera.2018.8566776

Cited by 22 publications

(14 citation statements)

References 12 publications

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“…The Simulink model is given in Figure 3. Equation (20) gives the transfer function (GðsÞ) of the bidirectional converter obtained from a MATLAB script.…”

Section: Simulation Resultsmentioning

confidence: 99%

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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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“…The Simulink model is given in Figure 3. Equation (20) gives the transfer function (GðsÞ) of the bidirectional converter obtained from a MATLAB script.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It is a combination of ANN and fuzzy logic (FL) [11,18,19]. In [20], the authors used ANFIS to extract the maximum available power across the PVG. The results obtained show the robustness and performance of the ANFIS command compared to the InC with response times of 2,4 ms and 401 ms, respectively.…”

Section: Introductionmentioning

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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“…It uses its linguistic variables to overcome this problem [35], [36]. Using the classical logic process, it has facility for extension and interaction [37], [38].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

Ndiaye¹,

Faye²,

Ndiaye³

2020

Adv. sci. technol. eng. syst. j.

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“…An effective solution to this problem, a MPPT controller is normally combined with an energy conversion to exalt the operating efficiency of photovoltaic systems [6]. Due to the simple implement and fast computing time, the MPPT controller using the P&O [7], and INC algorithm [8] are the most popular. The P&O algorithm is performed based on the disturbance of the output voltage, leading to the output power variation of PV.…”

Section: Introductionmentioning

confidence: 99%

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

Thanh

Nhu

Dong³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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For a photovoltaic system, the relationship of the output voltage and power is usually non-linear, so it is essential to equip a MPPT controller in PV systems. Furthermore, the hotspot problem is a common phenomenon, resulting from the PV system operating under PSC. Partial shading not only damages the PV cells, but also makes it difficult to find the global MPP in the characteristic curves of P-V. The paper proposes a novel version of PSO, namely PPSO in order to detect the global peak among the multiple peaks, known as the true maximum energy from PV panel. For this, the PPSO algorithm makes the velocity of each particle be perturbed once the particles are struck into a local minima state in order to find the best optimum solution in the MPPT problem. The perturbation in the velocity vector of each particle not only helps them tracking the MPP accurately under the changing environmental conditions, such as large fluctuations of insolation and temperature like PSC; but also removes the steady-state oscillation. The proposed approach has been tested on a MPPT system, which controls a dc-dc boost converter connected in series with a resistive load. Moreover, the obtained results are compared to those obtained without any MPPT controller to prove the efficiency of the suggested method. In addition, this novel version gives the highest accuracy of tracking the optimum power in the least iteration number as compared to the conventional PSO.

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Adaptive Neuro-Fuzzy Inference System Application for The Identification of a Photovoltaic System and The Forecasting of Its Maximum Power Point

Cited by 22 publications

References 12 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

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

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