Pem Fuel Cell With Conventional MPPT

Yamina, Jouili M; Garraoui, Radhia; Mouna, Ben Hamed

doi:10.1109/ssd49366.2020.9364218

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The working duty cycle of the DC/DC converter used for FC interface is set by the MPPT controllers during MPP operation. [18][19][20] In the base works, [21][22][23] several MPPT techniques such as perturb & observe (P&O), incremental conductance (INC), fuzzy logic control (FLC), adaptive neurofuzzy inference system (ANFIS), etc, for obtaining maximum power extraction from FCs are used. However, the baseline models [24][25][26] have the major drawbacks of high tracking time, reduced steady-state performance, high cost, and system complexity.…”

Section: Doi: 101002/ente202300556mentioning

confidence: 99%

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

Injarapu Edukondala Sanyasi,

Sambana,

Chilukoti

et al. 2023

Energy Tech

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The proton exchange membrane fuel cell (PEMFC), one of the many fuel cell varieties on the market, is distinguished by its low temperatures of operation, high reliability, and extended lifespan. The fuel cell system's maximum output can only be achieved at one specific operating point under varying operating circumstances. Hence, it is essential to get the most power possible from the PEMFC for improved functioning and optimal exploitation of grid systems. Thus, the design of a maximum power point tracking (MPPT) controller for a PEMFC power system using an amplified salp swarm optimization (AS2O) algorithm is focused on. Here, an interleaved Luo converter is also used to produce a regulated output voltage with less switching stress and frequency. Moreover, the proposed work intends to effectively satisfy the energy demand of grid systems by obtaining the maximum electrical energy from the PEMFCs with less harmonics and low system complexity. The simulation and performance results of the proposed AS2O‐MPPT controlling model are validated and compared using several parameters using MATLAB/Simulink tool. Using the proposed controlling technique, the total harmonics distortion is reduced up to 1.16%, with the settling time of 0.0062 s, rise time of 0.0010 s, and peak overshoot of 0.255 W.

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Section: Doi: 101002/ente202300556mentioning

confidence: 99%

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

Injarapu Edukondala Sanyasi,

Sambana,

Chilukoti

et al. 2023

Energy Tech

View full text Add to dashboard Cite

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“…The stator phase currents are a balanced system. The output voltages of the BLDC motor can be described by matrix equations (13).…”

Section: Fig4 Equivalent Circuit Of Bldcmentioning

confidence: 99%

“…Thus, a BLDC motor has a high power-to-mass ratio, less maintenance, and high-speed capabilities [9][10][11]. Various control schemes are created for speed regulation in a closed loop, such as the Proportional Integral Derivative (PID), optimization of PI coefficients using Genetic Algorithm (GA), Fuzzy Logic Controller (FLC), and Adaptive tuned Fuzzy Logic [12,13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Controller PI-Fuzzy Logic Speed for Brushless DC Motor Drive Supplied by PEMFC Cell Optimized by P&O

Jouili,

Garraoui,

Hamd

et al. 2023

International Journal on Applied Physics and Engineering

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Brushless Direct Current (BLDC) motors have recently gained momentum. In this study, a fuel cell stack, namely, a Proton-Exchange Membrane Fuel Cell (PEMFC), one of the promising renewable energy technologies, is chosen for a brushless DC motor. To improve the performance of PEMFC, an efficient maximum power point tracking (MPPT) algorithm was applied to the DC/DC boost converter. To this end, the perturbation and observation (P&O) algorithm were developed. This work proposes an adaptive controller proportional-integral (PI)-fuzzy logic speed for the BLDC. To evaluate its performance, the proposed controller was simulated under several conditions: load disturbance and reference speed variation. This controller is analyzed and compared with the classical PI controller. Therefore, the control performance parameters, such as rise time, settling time, steady-state error, and overshoot, were determined and compared. This system is analyzed and simulated using MATLAB/Simulink software.

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Self-Adaptive PI-FLC for BLDC Motor Speed Supplied by PEM Fuel Cell Stack Optimized by MPPT

Jouili,

Garraoui,

Ben Hamed

et al. 2023

Arab J Sci Eng

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Pem Fuel Cell With Conventional MPPT

Cited by 5 publications

References 10 publications

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

An Amplified Salp Swarm Optimization Algorithm for Maximum Power Point Tracking Control of Proton Exchange Membrane Fuel Cells

Adaptive Controller PI-Fuzzy Logic Speed for Brushless DC Motor Drive Supplied by PEMFC Cell Optimized by P&O

Self-Adaptive PI-FLC for BLDC Motor Speed Supplied by PEM Fuel Cell Stack Optimized by MPPT

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