Design and Hardware Implementation Based on Hybrid Structure for MPPT of PV System Using an Interval Type-2 TSK Fuzzy Logic Controller

Keçecioğlu, Ö. Fatih; Ganı, Ahmet; Şekkeli, Mustafa

doi:10.3390/en13071842

Cited by 29 publications

(24 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MPPT is performed, using a fuzzy logic controller to track the maximum operating point considering the mission profile [122], whereas in [123], a genetic algorithm is used to optimize the neural network controller for tracking the maximum point of operation. A brief analysis of different algorithms is presented in Table 8.…”

Section: Low Voltage Ride Through (Lvrt)mentioning

confidence: 99%

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

et al. 2021

View full text Add to dashboard Cite

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

show abstract

Section: Low Voltage Ride Through (Lvrt)mentioning

confidence: 99%

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To put it another way, each x input possesses a certain MF value ranging [0, 1]. If the MF degree of a variable is unknown, it must be replaced by a type 2 MF 24 . A type 2 fuzzy set involves triples, namely, x ∈ X , which is a primary MF value, u ∈ Jx ( Jx is the range of primary MF for a given x ), and a secondary MF.…”

Section: Overall Proposed Configurationmentioning

confidence: 99%

Improving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy‐neural network via EN 50530 test procedure

Ganı

2021

Circuit Theory & Apps

Self Cite

View full text Add to dashboard Cite

In recent years, photovoltaic generation systems (PGSs) have been fairly popular thanks to the methods proposed for the improvement of the dynamic efficiency. For a more accurate evaluation of the actual dynamic performance in PGSs, a dynamic maximum power point tracking (MPPT) efficiency test based upon an international test procedure such as the European Procedure EN 50530 is of vital importance. The varying gradient irradiance profile in EN 50530 test procedure causes uncertainties in PGSs, which lead to unstable operating conditions for PGSs. It is a challenging task to provide stable operation for PGSs. To overcome this problem, an effective MPPT method based upon adaptive type 2 fuzzy‐neural network (AT2FNN) is proposed using EN 50530 test procedure specifically designed for the dynamic performance of PGSs. The dynamic efficiency performance of the proposed method is analyzed using comparative simulation studies with conventional MPPT methods such as perturb and observe (P&O) and incremental conductance (IC) in Matlab/Simulink environment. The average dynamic efficiency of PGSs is used as performance criteria in the present study. The numerical results obtained from comparative simulation studies indicated that the proposed method improved average dynamic efficiency by almost 4.3% and 9.2% compared to IC and P&O MPPT methods, respectively. The average dynamic efficiency performance of PGS was considerably improved by the proposed method.

show abstract

“…Hence, FLC is popular in the last decade [20]. FLC can be integrated with HC algorithm such as P&O and IC [21]. FLC translates HC algorithm into fuzzy rules [22].…”

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

194

View full text Add to dashboard Cite

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).

show abstract

Design and Hardware Implementation Based on Hybrid Structure for MPPT of PV System Using an Interval Type-2 TSK Fuzzy Logic Controller

Cited by 29 publications

References 26 publications

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Improving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy‐neural network via EN 50530 test procedure

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

Contact Info

Product

Resources

About