Enhancing power quality in grid-connected hybrid renewable energy systems using UPQC and optimized O-FOPID DOI Creative Commons

R. Venkatesan,

C. Kumar,

C. R. Balamurugan

и другие.

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Июль 30, 2024

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests novel hybrid optimization technique that regulates UPQC order address the Power Quality (PQ) problems HRES system. The system serves primary link between battery energy storage systems (BESS), wind turbine (WT), solar photovoltaic (PV) components of major objective is PQ issues make up for requirement inside addition an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves efficiency UPQC. Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), enhanced variant traditional Tunicate (TSA) Crow Search (CSO), used optimize control parameters FOPID controller. Utilizing MATLAB/Simulink platform, method put into practice, system’s performance assessed sag, swell, Total Harmonic Distortion (THD). THD values PI, FOPID, CSA techniques, respectively, are 5.9038%, 4.9592%, 3.7027%, under sag condition. validates superiority approach over existing approaches.

Язык: Английский

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems DOI Creative Commons
M. Premkumar, Garima Sinha,

R. Manjula Devi

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 5, 2024

Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve optimization capabilities of conventional optimizer in order address problem data clustering. The process that groups similar items within dataset into non-overlapping groups. Grey hunting behaviour served as model for however, it frequently lacks exploration and exploitation are essential efficient work mainly focuses on enhancing using weight factor concepts increase variety avoid premature convergence. Using partitional clustering-inspired fitness function, was extensively evaluated ten numerical functions multiple real-world datasets with varying levels complexity dimensionality. methodology is based incorporating concept purpose refining initial solutions adding diversity during phase. results show performs much better than standard discovering optimal clustering solutions, indicating higher capacity effective solution space. found able produce high-quality cluster centres fewer iterations, demonstrating its efficacy efficiency various datasets. Finally, demonstrates robustness dependability resolving issues, which represents significant advancement over techniques. In addition addressing shortcomings algorithm, incorporation innovative establishes further metaheuristic algorithms. performance around 34% original both test problems problems.

Язык: Английский

Процитировано

31

Improved honey badger algorithms for parameter extraction in photovoltaic models DOI
Timur Düzenli̇, Funda Kutlu Onay, Salih Berkan Aydemı̇r

и другие.

Optik, Год журнала: 2022, Номер 268, С. 169731 - 169731

Опубликована: Июль 30, 2022

Язык: Английский

Процитировано

50

A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems DOI

Yuchen Duan,

Xiaobing Yu

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119017 - 119017

Опубликована: Окт. 14, 2022

Язык: Английский

Процитировано

50

A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules DOI

C. Kumar,

D. Magdalin Mary

Optik, Год журнала: 2022, Номер 264, С. 169379 - 169379

Опубликована: Май 27, 2022

Язык: Английский

Процитировано

43

Review of the grey wolf optimization algorithm: variants and applications DOI
Yunyun Liu, Azizan As’arry, Mohd Khair Hassan

и другие.

Neural Computing and Applications, Год журнала: 2023, Номер 36(6), С. 2713 - 2735

Опубликована: Ноя. 22, 2023

Язык: Английский

Процитировано

43

Electrical parameters extraction of PV modules using artificial hummingbird optimizer DOI Creative Commons
Ragab A. El‐Sehiemy, Abdullah M. Shaheen, Attia A. El‐Fergany

и другие.

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Июнь 7, 2023

The parameter extraction of PV models is a nonlinear and multi-model optimization problem. However, it essential to correctly estimate the parameters units due their impact on system efficiency in terms power current production. As result, this study introduces developed Artificial Hummingbird Technique (AHT) generate best values ungiven these units. AHT mimics hummingbirds' unique flying abilities foraging methods wild. compared with numerous recent inspired techniques which are tuna swarm optimizer, African vulture's teaching learning studying-based optimizer other techniques. statistical studies experimental findings show that outperforms extracting various STM6-40/36, KC200GT PWP 201 polycrystalline. AHT's performance evaluated using datasheet provided by manufacturer. To highlight dominance, its those competing simulation outcomes demonstrate algorithm features quick processing time steadily convergence consort keeping an elevated level accuracy offered solution.

Язык: Английский

Процитировано

35

Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem DOI Creative Commons

Yaolong Lu,

Siqi Liang, Haibin Ouyang

и другие.

Energy Reports, Год журнала: 2023, Номер 9, С. 4654 - 4681

Опубликована: Март 30, 2023

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve parameter identification problem. However, extracting reliable PV models still a great challenge, many HMAs may present unsatisfactory performance due their premature or slow convergence. Therefore, how develop algorithms efficiently balancing exploration exploitation improve accuracy reliability algorithm extremely important. This paper proposes Hybrid multi-group stochastic cooperative (HMSCPSO). In algorithm, we designed cooperation search mechanism enhance global capability: Each group utilized different strategies. first used classic velocity position updates, second employed chaos strategy, third lévy flight strategy. Through between groups increase diversity population reduce possibility falling into local optimum, but also concentrate some individuals explore current optimum solution. HMSCPSO its variants were tested on 27 benchmark functions verify algorithm's effectiveness. Then, applied four problems models. Statistical experiment results demonstrate that has excellent advantages compared with other in terms accuracy, reliability, convergence speed. expected be an effective method solar cells modules.

Язык: Английский

Процитировано

30

IGJO: An Improved Golden Jackel Optimization Algorithm Using Local Escaping Operator for Feature Selection Problems DOI

R. Manjula Devi,

M. Premkumar,

G. Kiruthiga

и другие.

Neural Processing Letters, Год журнала: 2023, Номер 55(5), С. 6443 - 6531

Опубликована: Фев. 14, 2023

Язык: Английский

Процитировано

27

Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review DOI Open Access

Zaiyu Gu,

Guojiang Xiong, Xiaofan Fu

и другие.

Sustainability, Год журнала: 2023, Номер 15(4), С. 3312 - 3312

Опубликована: Фев. 10, 2023

As the photovoltaic (PV) market share continues to increase, accurate PV modeling will have a massive impact on future energy landscape. Therefore, it is imperative convert difficult-to-understand systems into understandable mathematical models through equivalent models. However, multi-peaked, non-linear, and strongly coupled characteristics of make challenging extract parameters Metaheuristics can address these challenges effectively regardless gradients function forms, gained increasing attention in solving this issue. This review surveys different metaheuristics model parameter extraction explains multiple algorithms’ behavior. Some frequently used performance indicators measure effectiveness, robustness, accuracy, competitiveness, resources consumed are tabulated compared, then merits demerits algorithms outlined. The patterns variation results extracted from external environments were analyzed, corresponding literature was summarized. Then, for both application scenarios analyzed. Finally, perspectives research summarized as valid reference technological advances extraction.

Язык: Английский

Процитировано

26

Sub-population improved grey wolf optimizer with Gaussian mutation and Lévy flight for parameters identification of photovoltaic models DOI
Xiaobing Yu,

Yuchen Duan,

Zijing Cai

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 232, С. 120827 - 120827

Опубликована: Июнь 16, 2023

Язык: Английский

Процитировано

25