A Neural Network and Fuzzy Logic based MPPT Algorithm for Photovoltaic Pumping System

Assahout, Salwa; Elaissaoui, Hayat; Ougli, Abdelghani El; Tidhaf, Belkassem; Zrouri, Hafida

doi:10.11591/ijpeds.v9.i4.pp1823-1833

Cited by 38 publications

(35 citation statements)

References 17 publications

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“…Artificial Neural Network or ANN is a method that adapts the neuron system in the human brain into solving engineering or technology problem such as prediction values, classification, observation etc. ANN is very useful for modelling or predicting even though the input output data relationship is unknown [28][29][30][31][32]. A simple configuration of ANN is built up of three layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Nor

Salimin

Abdullah

et al. 2020

IJPEDS

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<span>Artiﬁcial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

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Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Nor

Salimin

Abdullah

et al. 2020

IJPEDS

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“…In several papers, neural network (NN) and fuzzy logic (FL) have been proposed as an MPPT . In these papers, NNs and FLs are used as an adaptive tracker to generate the duty cycle of a DC‐DC converter.…”

Section: Introductionmentioning

confidence: 99%

A self‐constructing Lyapunov neural network controller to track global maximum power point in PV systems

Tavakoli

Forouzanfar

2020

Int Trans Electr Energ Syst

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This study suggests an intelligent control method for achieving the maximum power point tracking (MPPT) in photovoltaic (PV) systems. MPPT technologies in PV systems are used to transfer maximum power under various environmental conditions. To improve the performance of the MPPT, the study develops a two‐level adaptive control structure that can facilitate system control and efficiently handle uncertainties and perturbations in the PV systems and in the environment. The first control level is a ripple correlation control (RCC), and the second is a model reference adaptive control (MRAC). The paper emphasizes mainly on designing an MRAC algorithm that improves the underdamped dynamic response of the PV system. The original state‐space equation of the PV system is time‐varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using a self‐constructing Lyapunov neural network (SCLNN), an adaptive law of the controller is derived for the MRAC system to remove the underdamped modes in the PV systems. Also, a self‐constructing mechanism for generating blocks in the recursive unit of the SCLNN is introduced. Since the size of the SCLNN is optimal and minimal, the computation time, which is an important factor for real‐time implementation, is greatly reduced. It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds.

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“…For MPPT techniques based on soft computing (SC), we can mention those that are used most often, such as fuzzy logic methods [13 14], bio-inspired methods like artificial neural networks (ANN) technique [15,16] and the swarm intelligence methods like Ant Colony Optimization (ACO) [17] and Particle Swarm Optimization (PSO) [18]. Regardless of their flexibility, these SC algorithms are mostly more difficult and slower than conventional methods.…”

Section: Introductionmentioning

confidence: 99%

A new high performance variable step size perturb-and-observe MPPT algorithm for photovoltaic system

Saidi¹,

Maamoun²,

Bounekhla³

2019

IJPEDS

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The optimization of energy in a photovoltaic conversion chain remains a topic of study. The maximum power point tracking (MPPT) is considered the most appropriate solution to ensure the extraction of the maximum power which a photovoltaic generator can provide. Perturb and observe (P&O) algorithm is the well-known MPPT tracker; it shows an important and decisive role in the development of photovoltaic (PV) systems. In This article we introduce a new improved adjustable step size P&O (imVS_PO) MPPT method.The performances of the new algorithm are compared to the conventional P&O MPPT techniques and to the existing adjustable step size P&O (VS_PO) MPPT algorithm. The results clearly show the efficiency of the improved algorithm which contributed to the increase of the tracking speed with negligible fluctuations when the MPP is reached, and especially in rapid variation of insolation.

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A Neural Network and Fuzzy Logic based MPPT Algorithm for Photovoltaic Pumping System

Cited by 38 publications

References 17 publications

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

A self‐constructing Lyapunov neural network controller to track global maximum power point in PV systems

A new high performance variable step size perturb-and-observe MPPT algorithm for photovoltaic system

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