Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems

Youssef, Ayman; El-Telbany, Mohammed E.; Zekry, Abdelhalim

doi:10.1016/j.rser.2017.09.093

Cited by 73 publications

(40 citation statements)

References 8 publications

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“…This method has simple implementation and inexpensive but accurate MPP is not accessible (Das, 2016). Many researchers have worked on fuzzy logic and neural network and reported good convergence and rapid speed but these algorithms need advanced hardware and complex programming (Harrag and Bahri, 2017; Messalti et al, 2017, Ozdemir et al, 2017; Youssef et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Robust maximum power point tracking with a novel second order sliding mode controller

Fesharaki

Amnyieh

Fatemi

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper proposes a robust second order sliding mode controller as maximum power point tracking (MPPT) technique in a photovoltaic (PV) boost dc-dc converter with applications to stand-alone systems. The proposed method is independent respect to load type, robust against parametric uncertainties and disturbances. By Lyapunov theorem the asymptotic stability of the closed loop control system is proven. The proposed second order sliding mode controller is simulated with Matlab software and experimental set up in presence of sinusoidal disturbances on output voltage.

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Section: Introductionmentioning

confidence: 99%

Robust maximum power point tracking with a novel second order sliding mode controller

Fesharaki

Amnyieh

Fatemi

et al. 2020

Transactions of the Institute of Measurement and Control

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“…In the defuzzification phase, the FLC output is changed over from a semantic variable to a numerical variable and the analogue signal provided by this process will drive the power converter to the edge of MPP. The calculation related to this technique and the implementation of the controller for PV applications is detailed in [43,[86][87][88].…”

Section: Intelligent Prediction Based Methodsmentioning

confidence: 99%

MPPT methods for solar PV systems: a critical review based on tracking nature

Podder

Roy

Pota

2019

IET Renewable Power Generation

310

135

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An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the current status of MPPT methods for PV systems which are classified into eight categories. The categorisation is based on the tracking characteristics of the discussed methods. The novelty of this study is that it focuses on the key characteristics and eleven selection parameters of the methods to make a comprehensive analysis, which is not considered together in any review works so far. Again, the pros and cons, classification and immense comparison among them described in this study can be used as a reference to address the gaps for further research in this field. A comparative review in tabular form is also presented at the end of the discussion of each category to evaluate the performance of these methods, which will help in selecting the appropriate technique for any specific application.

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“…The solar power is dependent on the dynamic of solar irradiance [25]. Additionally, FLC is reconfigurable and highly flexible because it can be reprogrammable through a field-programmable gate array (FPGA) [26]. FLC is a relatively simpler, cost-effective and historically older implementation of CI in MPPT.…”

Section: A Flcmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

197

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems

Cited by 73 publications

References 8 publications

Robust maximum power point tracking with a novel second order sliding mode controller

Robust maximum power point tracking with a novel second order sliding mode controller

MPPT methods for solar PV systems: a critical review based on tracking nature

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

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