IC-based Variable Step Size Neuro-Fuzzy MPPT Improving PV System Performances

Harrag, Abdelghani; Messalti, Sabir

doi:10.1016/j.egypro.2018.11.201

Cited by 46 publications

(25 citation statements)

References 19 publications

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“…Harrang et al have proposed IC based variable step size ANFIS controller for MPPT of photovoltaic system under various atmospheric conditions. For the input conditions of voltage and current, the proposed IC‐based neuro‐fuzzy model predicts pulse width modulation 150 …”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Recent advancements to improve the INC method are presented in [34]- [36]. A fuzzy logic based control is used for improving the INC method was presented in [37]- [39]. A fuzzy logic based auto-scaling variable step-size MPPT method is presented in [40].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

et al. 2021

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Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units. In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV. In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation. For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power. To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions. The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed. The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method. The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT. The proposed method is verified and tested through the simulation of a grid-connected PV system model. The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions. Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions. INDEX TERMS Maximum power point tracking, fuzzy logic, incremental conductance, PV system, dynamic responses.

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“…The low conversion efficiency of solar cells is the main difficulty that prevents their widespread use in the transportation sector. Improving the conversion efficiency of PV cells is not easy, as it depends on the currently available technology [13]. Consequently, how to generate the maximum amount of energy is one problem that must be solved.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies demonstrate the capability of ANNs as MPPT techniques [43]. In this manner, the input signals to the ANN can be electrical (voltage and current) [13,[44][45][46], non-electrical (temperature and irradiance) [47][48][49][50], or any combination of these [8,[51][52][53]. Generally, electrical inputs are preferred to non-electrical inputs from cost and robustness points of view [8].…”

Section: Introductionmentioning

confidence: 99%

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

2021

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The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.

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IC-based Variable Step Size Neuro-Fuzzy MPPT Improving PV System Performances

Cited by 46 publications

References 19 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

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