Differential Evolution-based MPPT with Dual Mutation for PV Array under Partial Shading Condition

Firmanza, Ade Pradana; Habibi, Muhammad Nizar; Windarko, Novie Ayub; Yanaratri, Diah Septi

doi:10.1109/eeccis49483.2020.9263430

Cited by 12 publications

(7 citation statements)

References 15 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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“…Penelitian penerapan metode ANN dilakukan pada [31] dan [32]. Pada [31], penelitian dilakukan melalui simulasi dengan software PSIM.…”

Section: Metode Differential Evolution (De)unclassified

“…Penelitian penerapan metode ANN dilakukan pada [31] dan [32]. Pada [31], penelitian dilakukan melalui simulasi dengan software PSIM. Simulasi dilakukan dengan kondisi partial shading dengan empat pola shading dengan variasi nilai iradiasi yaitu 600 -1000 W/m 2 .…”

Section: Metode Differential Evolution (De)unclassified

Metode Maximum Power Point Tracking pada Panel Surya : Sebuah Tinjauan Literatur

Wirsuyana

Hartati²,

Manuaba³

2022

tech

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Metode pelacakan titik daya maksimum (MPPT) yang efisien memainkan peran penting untuk meningkatkan efisiensi sistem pembangkit fotovoltaik (PV). Studi ini memberikan tinjauan tentang status metode MPPT saat ini untuk sistem PV yang diklasifikasikan ke dalam tiga kategori. Kategorisasi didasarkan pada karakteristik pelacakan dari metode yang dibahas. Pada penelitian ini juga dilakukan perbandingan antar metode MPPT berdasarkan tiga parameter yaitu waktu konvergensi, tingkat osilasi, dan efisiensi sebagai acuan pemilihan metode untuk membuat analisis yang komprehensif. Tinjauan literatur yang dijelaskan dalam penelitian ini dapat digunakan sebagai referensi untuk mengatasi kesenjangan untuk penelitian lebih lanjut di bidang ini. Tinjauan komparatif dalam bentuk tabel juga disajikan di akhir pembahasan setiap kategori untuk mengevaluasi kinerja metode ini, yang akan membantu dalam memilih teknik yang sesuai untuk aplikasi tertentu. Hasil yang didapatkan adalah metode konvensional unggul pada waktu tracking yang cepat sedangkan metode metaheuristic unggul pada efisiensi dan osilasi yang rendah.

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“…Over the years, there has been a multitude of publications on tracking the MPP of a shaded PV array in the literature. Here are a number of frequently used algorithms on this issue: fuzzy logic control algorithms [12][13][14], neural networks (NN) [15][16][17], the grey wolf optimization (GWO) algorithm [18][19][20], the differential evolution (DE) algorithm [21][22][23], the artificial bee colony (ABC) algorithm [24][25][26], the firefly algorithm (FA) [27][28][29][30], the variable step size perturbation and observation (P&O) [31] and the shuffled frog-leaping (SFL) algorithm [32]. In the fuzzy control algorithms [12][13][14], input data are converted into fuzzy data, a membership function is used to map the fuzzy data into member attributes.…”

Section: Introductionmentioning

confidence: 99%

“…Although the GWO algorithm is simple and requires a smaller number of parameters to run global optimization, its major disadvantages include poor tracking accuracy, slow convergence and particularly the likelihood of getting stuck in a local solution. The DE algorithm in [21][22][23] is essentially a heuristic model and works in a similar way to the genetic algorithm (GA). It performs real number coding on a specific population.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Maximum Power Point Tracker for Photovoltaic Arrays Using an Improved Soft Computing Algorithm

Chao

Zhang

2023

Applied Sciences

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This paper presents an improved version of the firefly algorithm (FA) by which a maximum power point (MPP) tracker was developed to track down the global maximum power point (GMPP) of a partially shaded photovoltaic module array (PVMA). As the first step, our team developed a high-voltage step-up converter where a coupled inductor was used to store the energy so that the duty cycle can be reduced so as to raise the voltage gain. The single-peaked P-V output characteristic curve of a PV array turns out to contain multiple peaks when the array is partially shaded. As a consequence, conventional MPP trackers occasionally track down a local maximum power point (LMPP), instead of the desired GMPP, and the output power of the array falls accordingly. Therefore, an improved version of the FA is proposed as a way to ensure that the GMPP can be tracked down in a more efficient way. Using the Matlab software, the MPP tracking performance of the proposed tracker was finally simulated in five scenarios. As it turned out, the proposed converter provided a high voltage gain at a relatively low duty cycle, and the improved version of the FA outperformed the original in terms of tracking time.

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Differential Evolution-based MPPT with Dual Mutation for PV Array under Partial Shading Condition

Cited by 12 publications

References 15 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

Metode Maximum Power Point Tracking pada Panel Surya : Sebuah Tinjauan Literatur

An Adaptive Maximum Power Point Tracker for Photovoltaic Arrays Using an Improved Soft Computing Algorithm

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