Dynamic Multi-peak MPPT for Photovoltaic Power Generation Under Local Shadows Based on Improved Mayfly Optimization

Yi, Lingzhi; Shi, Hao; Liu, Jiangyong; Zhou, Donghua; Liu, Ximeng; Zhu, Jiang

doi:10.1007/s42835-021-00855-w

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…As seen in figure 7, the power enhancement of the PV array is related to the light intensity and roughly maintains an isotropic series of variations [35,36].…”

Section: Reconfiguration Options For Shading Of Rpvpgsmentioning

confidence: 92%

A multivariate reconfiguration method for rooftop PV array based on improved northern goshawk optimization algorithm

Yi,

Cheng,

Wang

et al. 2024

Phys. Scr.

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Photovoltaic (PV) power has become a crucial solution to the escalating energy crisis. Among the various implementations, Rooftop PV power generation systems (RPVPGS) are predominant in PV buildings. However, RPVPGS will face challenges such as reduced output power due to array fault or shading, leading to fluctuations in Building-Integrated PV (BIPV) power generation. This paper attempts to solve this problem by proposing a novel multivariate reconfiguration method based on the improved northern goshawk optimization algorithm (INGO). The aim is to find the optimal state of RPVPGS under various conditions. In this paper, extensive simulations were conducted on the experimental platform to assess the feasibility and effectiveness of the proposed method. It is worth noting that INGO outperforms existing technologies such as Arrow SoDuku and Zig-zag for the evaluation metrics mentioned in the article. Furthermore, rigorous simulation experiments were conducted on the semi-physical platform to validate the proposed approach. The power enhancement percentage deviation was between +0.1% to +0.2%. These results unequivocally demonstrate that the INGO-based multivariate reconfiguration method accurately reconfigures RPVPGS, ensuring the efficiency and stability of BIPV systems.

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“…As seen in figure 7, the power enhancement of the PV array is related to the light intensity and roughly maintains an isotropic series of variations [35,36].…”

Section: Reconfiguration Options For Shading Of Rpvpgsmentioning

confidence: 92%

A multivariate reconfiguration method for rooftop PV array based on improved northern goshawk optimization algorithm

Yi,

Cheng,

Wang

et al. 2024

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This exploration principally focuses on an exact displaying of the proton trade layer energy unit (PEMFC) that gives a great match between the reenactment results and those deliberate for all intents and purposes. Yi et al, [18] projected Dynamic Multi-peak MPPT for electrical phenomenon Power Generation underneath native Shadows supported improved mayfly optimization.…”

Section: Mayfly Algorithmmentioning

confidence: 99%

A proposed framework for face - iris recognition system using enhanced mayfly algorithm

Oladimeji¹,

Asaju-Gbolagade²,

Gbolagade³

2022

Nig. J. Tech.

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Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.

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“…The nature-inspired MPPT techniques like the marine predator algorithm [14][15][16][17], and the Mayfly optimization algorithm provides increased efficiency, precision, and quick retort in pursuing the global maximum under partial shading as associated to other algorithms [18]. Nature-inspired optimization methods are popular due to their stochastic search nature and clear concept, making them simple to use [19]. In [20], a new optimization technique, named EPO, was introduced to improve the early duty cycle regulation and the tuning of controller gains in the boost converter.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

Sonam Soma,

R.,

2024

TESEA

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PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

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Dynamic Multi-peak MPPT for Photovoltaic Power Generation Under Local Shadows Based on Improved Mayfly Optimization

Cited by 9 publications

References 11 publications

A multivariate reconfiguration method for rooftop PV array based on improved northern goshawk optimization algorithm

A multivariate reconfiguration method for rooftop PV array based on improved northern goshawk optimization algorithm

A proposed framework for face - iris recognition system using enhanced mayfly algorithm

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

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