Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT

Amara, Karima; Fekik, Arezki; Hocine, D.; Bakir, Mohamed Lamine; Bourennane, El-Bay; Malek, Toufik Ali; Malek, A.

doi:10.1109/icrera.2018.8566818

Cited by 68 publications

(41 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…depends also on a parameter which optimal value (allowing to get the optimal update of ) is based on the first and the second derivatives of (gradient and hessian, respectively). Before giving details of OSGM method, let's introduce our previous proposed algorithm (ANFIS-based MPPT method) which is a dependent method [4]. P&O, GM, ANFIS-based MPPT and OSGM methods will be compared in simulation results section.…”

Section: B Gradient Descent Methods (Gm)mentioning

confidence: 99%

“…As abovementioned, we present P&O and GM MPPT algorithms, we present also ANFIS based MPPT algorithm which was proposed in our previous work [4]. Finally we proposed our new MPPT algorithm (OSGM).…”

Section: Mppt Techniquesmentioning

confidence: 98%

“…Following these conditions (irradiance and temperature), Vref and/or duty cycle corresponding to the MPP are selected from the (Vref and or/duty cycle) database [1,2]. In the case of dependent methods, among different intelligent controllers like Artificial Neural Controller (ANN) [3], Adaptive Neuro-Fuzzy Inference System (ANFIS) [4], the Fuzzy Logic Controller (FLC) is the simplest to implement. Recently, FLC received an increasing attention from researchers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

Amara¹,

Bakir²,

Malek³

et al. 2019

ijeei

Self Cite

View full text Add to dashboard Cite

In order to improve the photovoltaic (PV) production, the researchers are interested in developing new methods to reach the Maximum Power Point (MPP) produced by the photovoltaic field to be injected into the utility grid. This article describes a new method called the Optimized Steepest Gradient Method (OSGM), it is based on the first (gradient) and second order (hessian) derivatives of the power function in order to find the best variation of the voltage (Vpv) with the calculation of the optimal step allowing the convergence to the tension value (Vref) which ensures the MPP. The mathematical model has been developed and implemented under Matlab/Simulink environment. To analyze Maximum Power Point Tracking (MPPT) algorithm performances, time response, oscillation, overshoot and stability are taken into account. The OSGM is implemented and compared to three others algorithms (one of these algorithm is the ANFIS proposed in previous work). Performances obtained by the proposed algorithm offer faster response, less oscillations around MPP and a low energy loss. In addition, numerical computation of the gradient and the hessian of the power function allow bypassing modeling inaccuracies.

show abstract

Section: B Gradient Descent Methods (Gm)mentioning

confidence: 99%

Section: Mppt Techniquesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

Amara¹,

Bakir²,

Malek³

et al. 2019

ijeei

Self Cite

View full text Add to dashboard Cite

show abstract

“…The combination between fuzzy logic and the neural network offers the advantages of both networks (human-like IF-THEN rules thinking, ease of incorporating expert knowledge, learning abilities, optimization abilities, and connectionist structures) [15][16][17][18]. For the present work, the fuzzy neural network controller is utilized to overcome the drawbacks of the individual techniques and control the PV output power to extract MPP.…”

Section: Fuzzy Neural Network (Fnn) Controllermentioning

confidence: 99%

Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network

et al. 2019

View full text Add to dashboard Cite

The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel and produces an optimal value of duty ration for the boost converter to obtain the MPPT. The coefficients of this controller have been refined based upon previous data sets using the irradiance and temperature. A gradient descent algorithm is employed to improve the parameters of the FNN controller to achieve an optimal response. The validity of the PV system using the MPPT technique based on the FNN controller is further demonstrated via a series of experimental tests at different ambient conditions. The simulation results show how the MPPT technique based on the FNN controller is more effective in maintaining the optimal power values compared with conventional techniques.

show abstract

“…The comparison of the results shows that the power of the PVG increases by 83% for an illumination of 500 W/m 2 and 97% for an illumination of 600 W/m 2 with the MPPT command-type ANFIS. ANFIS is compared with the MPPT P&O command in [22]. Used to extract the maximum power from a PV system, the results showed that under varying lighting conditions, the best performances are obtained with ANFIS.…”

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

View full text Add to dashboard Cite

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.

show abstract

Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT

Cited by 68 publications

References 14 publications

An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems

Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network

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

Contact Info

Product

Resources

About