Hardware implementation of the fuzzy logic MPPT in an Arduino card using a Simulink support package for PV application

Fannakh, Mhamed; Elhafyani, Mohamed Larbi; Zouggar, Smail

doi:10.1049/iet-rpg.2018.5667

Cited by 56 publications

(24 citation statements)

References 20 publications

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“…This section demonstrates the real‐time feasibility of TVF algorithm using the personal computer (PC) with the MATLAB software and the Arduino Due with a 32 bit Atmel SAM3X8E ARM Cortex‐M3 processor having 512 kB flash memory with 96 kB SRAM and 84 MHz clock speed [49]. The modal voltage signal is extracted from the test system of Fig.…”

Section: Hil Implementation Of Tvf Algorithmmentioning

confidence: 99%

Islanding detection using total variation‐based signal decomposition technique

Mishra

Bhende

Manikandan

2019

IET Energy Systems Integration

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This study presents a passive islanding detection technique based on the approach of total variation filtering (TVF) for inverter-based distributed generation under noisy environment. Contamination of noise in the signal makes the islanding detection threshold less reliable. Therefore, the modal voltage signal is analysed through TVF-based decomposition procedure and several statistical features are obtained from its output. For decision making, the features are compared with the prespecified threshold. To tackle the challenge of islanding detection in the presence of the high-quality factor load, phase angle of the positive sequence voltage is considered as the second level of detection criterion. The proposed scheme is validated on IEEE 34-bus standard distribution system. The scheme is tested under islanding as well as all possible non-islanding network disturbances. Hardware-in-loop results are also presented in support of the theory. A comparative analysis of the proposed algorithm with the well-established S-transform is presented. The proposed scheme is found to be working effectively and can detect islanding condition within three cycles of its inception.

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Section: Hil Implementation Of Tvf Algorithmmentioning

confidence: 99%

Islanding detection using total variation‐based signal decomposition technique

Mishra

Bhende

Manikandan

2019

IET Energy Systems Integration

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“…Researchers developed various techniques to extract maximum power from the PV sources. Some of the MPPT techniques are perturb and observe (P&O) [5][6][7], hill climbing (HC) [8], incremental conductance (IncCond) [9,10], fractional voltage/current MPPT control [11], fuzzy-logic (FL) [12,13], neural network (NN) [14,15], optimization techniques [16], and sliding mode (SM) control [17][18][19]. Among the conventional MPPT techniques, P&O and the IncCond techniques are widely used due to their simplicity yet being efficient [20].…”

Section: Introductionmentioning

confidence: 99%

A current sensor based adaptive step‐size MPPT with SEPIC converter for photovoltaic systems

Kumar

Ghosh

Naidu

et al. 2021

IET Renewable Power Gen

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Efficient maximum power point tracking (MPPT) is an important problem for renewable power generation from photovoltaic systems. In this work, a current sensor based MPPT algorithm using an adaptive step-size for a single ended primary inductance converter (SEPIC) based solar photovoltaic system is proposed. Due to lower sensitivity of power to current perturbation as compared to the voltage one, such a scheme is shown to yield better efficiency at steady-state. A new adaptation scheme is also proposed for faster convergence of the MPPT technique. Hence, the proposed scheme yields better transient as well as steady-state performance. A prototype converter is used along with digital implementation of the proposed MPPT technique to demonstrate the superiority of the proposed algorithm over the fixed step-size and voltage based ones. Simulation and experimental results corroborates the same. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…On the other hand, Arduino is a microcontroller family and a software creation environment that allows developing programs to interact with the physical world. Due to its agile development capabilities and facility for quick implementation of ideas, many applications have been presented, such as those found in [11][12] [13]. In [11], it was demonstrated that with adaptable Simulink models and a wide number of libraries for the Arduino IDE, the system allows electromyographic (EMG) processing as well as basic classification for actuating both basic hand models and advanced hand prostheses.…”

Section: Introductionmentioning

confidence: 99%

“…All data are evaluated using multivariate data analysis through a customized code written in Matlab. Finally, in [13], an intelligent algorithm is developed, based on fuzzy logic, to track the maximum power point (MPP) of a photovoltaic (PV) panel using Simulink Support Package for Arduino Hardware in MATLAB/Simulink. According to Figure 1, a six-sided dice is an external signal input.…”

Section: Introductionmentioning

confidence: 99%

A real-time pattern recognition module via Matlab-Arduino interface

Díaz¹,

Pardo²,

Suárez³

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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Pattern recognition is a relevant research area in artificial vision, where several methods have been proposed in the last 50 years. This paper presents a real-time pattern recognition algorithm for an addition operation through two six-sided dice using an Android camera device, an IP webcam app, a graphical user interface (GUIs) from Matlab, and Arduino technology. The methodology to develop the interface and the communication between Matlab software and Arduino technology is presented. To evaluate the performance of the proposed methodology, a real-time implementation using an Arduino Mega 2560 board and Matlab is illustrated.

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Hardware implementation of the fuzzy logic MPPT in an Arduino card using a Simulink support package for PV application

Cited by 56 publications

References 20 publications

Islanding detection using total variation‐based signal decomposition technique

Islanding detection using total variation‐based signal decomposition technique

A current sensor based adaptive step‐size MPPT with SEPIC converter for photovoltaic systems

A real-time pattern recognition module via Matlab-Arduino interface

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