Maximum Power Extraction from Polymer Electrolyte Membrane (PEM) Fuel Cell Based on Deterministic Particle Swarm Optimization Algorithm

Elbaz, Ahmed Mohamed; Elfar, Medhat H.; Kalas, Ahmed E.; Refaat, Ahmed

doi:10.1109/elconrus54750.2022.9755807

Cited by 11 publications

(2 citation statements)

References 16 publications

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“…Many studies have evaluated various meta-heuristic optimization strategies for tracking the maximum power point in renewable energy (RE) plants. FSSO 67,68 , and CS 69 are better at efficiency, accuracy, resilience, and time to convergence than Genetic Algorithms (GA) 70,71 , Differential Evolution (DE) 72,73 , or Particle Swarm Optimization (PSO) [74][75][76] .…”

Section: Evaluation Of Performancementioning

confidence: 99%

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Bouguerra,

Badoud,

Mekhilef

et al. 2024

Sci Rep

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This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

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Section: Evaluation Of Performancementioning

confidence: 99%

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Bouguerra,

Badoud,

Mekhilef

et al. 2024

Sci Rep

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show abstract

“…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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Sustainability Research at Port Said University Towards the Achievement of the Sustainable Development Goals

Elsakka,

Bassyouni,

Rizk

et al. 2024

Earth and Environmental Sciences Library

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Maximum Power Extraction from Polymer Electrolyte Membrane (PEM) Fuel Cell Based on Deterministic Particle Swarm Optimization Algorithm

Cited by 11 publications

References 16 publications

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

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

Sustainability Research at Port Said University Towards the Achievement of the Sustainable Development Goals

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