Harmonic estimator design using improved gazelle optimization algorithm by chaos for real field signal DOI
Mustafa Saka, Melih Çoban

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126186 - 126186

Published: Dec. 1, 2024

Language: Английский

A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models DOI
Davut İzci, Serdar Ekinci,

Maryam Altalhi

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: 106(5), P. 6565 - 6585

Published: April 20, 2024

Language: Английский

Citations

15

Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response DOI Creative Commons
Yujun Zhang, Yufei Wang,

Yuxin Yan

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 98, P. 364 - 389

Published: May 11, 2024

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold proposed, which AHCSMA, efficiently. Specifically, there three innovations paper: (i) new Cauchy operator developed improve ability of population; (ii) crossover rate balance mechanism proposed make up for neglected relationship between individuals rates. Then differential vector information dominant individual other population utilized increase evolution speed algorithm; (iii) restart opposition learning designed alleviate situation where falls into local optimality. To verify competitive UAV path planning problems, engineering nonlinear parameter extraction photovoltaic model identification infinite impulse response used test accumulation more than 50 algorithms as comparison algorithms, results report that AHCSMA extremely performs better when optimizing real-life

Language: Английский

Citations

14

Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems DOI
Mahmoud Abdel-Salam, Heba Askr, Aboul Ella Hassanien

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124882 - 124882

Published: July 29, 2024

Language: Английский

Citations

14

Dynamic load frequency control in Power systems using a hybrid simulated annealing based Quadratic Interpolation Optimizer DOI Creative Commons
Davut İzci, Serdar Ekinci, Emre Çelik

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

Ensuring the stability and reliability of modern power systems is increasingly challenging due to growing integration renewable energy sources dynamic nature load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach frequency control (LFC) by proposing filtered PID (PID-F) controller optimized through hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines local search capabilities (SA) with global optimization strengths (QIO), providing robust efficient solution for LFC challenges. key contributions this study include development application hSA-QIO, which significantly enhances performance PID-F controller. proposed was evaluated on unimodal, multimodal, low-dimensional benchmark functions, demonstrate its robustness effectiveness across diverse scenarios. results showed significant improvements quality compared original QIO, lower objective function values faster convergence. Applied two-area interconnected system photovoltaic-thermal generation, hSA-QIO-tuned achieved substantial reduction integral time-weighted absolute error 23.4%, from 1.1396 0.87412. Additionally, reduced settling time deviations Area 1 9.9%, 1.0574 s 0.96191 s, decreased overshoot 8.8%. In 2, improved 0.89209 4.8%. also demonstrated superior tie-line regulation, achieving immediate response minimal overshoot.

Language: Английский

Citations

11

Parameter extraction of photovoltaic cell models using electric eel foraging optimizer DOI Creative Commons
Davut İzci, Serdar Ekinci, Laith Abualigah

et al.

Frontiers in Energy Research, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 1, 2024

Solar energy has emerged as a key solution in the global transition to renewable sources, driven by environmental concerns and climate change. This is largely due its cleanliness, availability, cost-effectiveness. The precise assessment of hidden factors within photovoltaic (PV) models critical for effectively exploiting potential these systems. study employs novel approach parameter estimation, utilizing electric eel foraging optimizer (EEFO), recently documented literature, address such engineering issues. EEFO emerges competitive metaheuristic methodology that plays crucial role enabling extraction. In order maintain scientific integrity fairness, utilizes RTC France solar cell benchmark case. We incorporate approach, together with Newton-Raphson method, into tuning process three PV models: single-diode, double-diode, three-diode models, using common experimental framework. selected because significant field. It serves reliable evaluation platform approach. conduct thorough statistical, convergence, elapsed time studies, demonstrating consistently achieves low RMSE values. indicates capable accurately estimating current-voltage characteristics. system’s smooth convergence behavior further reinforces efficacy. Comparing competing methodologies advantage optimizing model parameters, showcasing greatly enhance usage energy.

Language: Английский

Citations

8

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique DOI Creative Commons
Shubhranshu Mohan Parida, Vivekananda Pattanaik, Subhasis Panda

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 16, 2025

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling critical due to many factors, such as inherent nonlinearity, existing complexity, wide range parameters. Although different researchers have recently proposed several effective techniques system parameter identification, it still an interesting challenge enhance accuracy modelling. With above motivation, this article suggests stage-specific mutation strategy enhanced differential evolution (EDE) that adopts better search process arrive at optimal solutions by adaptively varying factor crossover rate stages. The identification systems formulated single objective function. It appears in form Root Mean Square Error (RMSE) between current from experimental data calculated using identified considering constraints (limits). I-V (current-voltage) characteristics/data with are validated justify approach's efficacy cells modules. Extensive simulation has been demonstrated two (RTC France & PVM-752-GaAs) three modules (ND-R250A5, STM6 40/36 STP6 120/36). results obtained EDE technique show Errors 7.730062e-4, 7.419648e-4, 7.33228e-4 respectively, RTC models based on single, double, triple diodes. Also, RMSE involved PVM-752-GaAs diodes 1.59256e-4, 1.408989e-4, 1.30181e-4, respectively. ND-R250A5, 120/36 involve values 7.697716e-3, 1.772095e-3, 1.224258e-2, All these least compared other well-accepted algorithms, thereby justifying its higher accuracy.

Language: Английский

Citations

1

Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms DOI Open Access
Eduardo Bayona, Jesús Enrique Sierra-García, Matilde Santos

et al.

Expert Systems, Journal Year: 2025, Volume and Issue: 42(3)

Published: Jan. 22, 2025

ABSTRACT In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas cross narrow corridors with strict safety time requirements. Bio‐inspired optimization algorithms have emerged as a promising approach to deal complex scenarios. Thus, this paper explores ability three novel bio‐inspired algorithms: Bat Algorithm (BA), Whale Optimization (WOA) Gazelle (GOA); optimise AGV path planning environments. To do it, new strategy described: trajectory based on clothoid curves specialised piece‐wise fitness function which prioritises designed. Simulation experiments were conducted across different occupancy maps evaluate performance each algorithm. WOA demonstrates faster providing suitable solutions 4 times than GOA. Meanwhile, GOA gives better metrics but demands more computational time. The study highlights potential approaches optimisation suggests avenues future research, including hybrid algorithm development.

Language: Английский

Citations

0

A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models DOI Creative Commons

Adel Zga,

Farouq Zitouni, Saad Harous

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2646 - e2646

Published: Jan. 27, 2025

This study conducts a comparative analysis of the performance ten novel and well-performing metaheuristic algorithms for parameter estimation solar photovoltaic models. optimization problem involves accurately identifying parameters that reflect complex nonlinear behaviours cells affected by changing environmental conditions material inconsistencies. is challenging due to computational complexity risk errors, which can hinder reliable predictions. The evaluated include Crayfish Optimization Algorithm, Golf Coati Crested Porcupine Optimizer, Growth Artificial Protozoa Secretary Bird Mother Election Optimizer Technical Vocational Education Training-Based Optimizer. These are applied solve four well-established models: single-diode model, double-diode triple-diode different module focuses on key metrics such as execution time, number function evaluations, solution optimality. results reveal significant differences in efficiency accuracy algorithms, with some demonstrating superior specific Friedman test was utilized rank various revealing top performer across all considered optimizer achieved root mean square error 9.8602187789E-04 9.8248487610E-04 both models 1.2307306856E-02 model. consistent success indicates strong contender future enhancements aimed at further boosting its effectiveness. Its current suggests potential improvement, making it promising focus ongoing development efforts. findings contribute understanding applicability renewable energy systems, providing valuable insights optimizing

Language: Английский

Citations

0

A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation DOI Creative Commons
Ahmed Jeridi, Med Hedi Moulahi,

Hechmi Khaterchi

et al.

Power Electronics and Drives, Journal Year: 2025, Volume and Issue: 10(1), P. 41 - 59

Published: Jan. 1, 2025

Abstract Accurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence difficulty balancing exploration exploitation. This study introduces a novel hybrid approach, WSO-HO, which integrates strengths war strategy optimization (WSO) Hippopotamus Optimization (HO) algorithms, enhanced by Newton-Raphson (NR) method, to achieve precise PV models. The effectiveness WSO-HO algorithm was rigorously evaluated through intensive testing on three different solar panels, including RTC France cell using single diode model (SDM) double (DDM), over 30 iterations. Comparative analysis highlights superior against conventional struggle with accurately identifying parameters. These results demonstrate significant potential this approach improve optimisation in systems, enabling more overall system efficiency. Furthermore, simulation result benchmarked other reported literature, further validating its robustness effectiveness.

Language: Английский

Citations

0

Parameters estimation of PV models using a novel hybrid equilibrium optimization algorithm DOI Creative Commons

Fude Duan,

Ali B. M. Ali,

Dheyaa J. Jasim

et al.

Energy Exploration & Exploitation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

This study presents a novel algorithm, termed equilibrium optimizer-single candidate optimizer (EO-SCO), which combines the EO and SCO techniques. The objective of this approach is to achieve accurate reliable parameter estimates for photovoltaic (PV) solar cells modules. EO-SCO, as outlined, functions through two-phase approach. first phase uses an pool elite particles traverse search space find interesting places using EO, retaining solution diversity. second integrates lead searching toward better vicinities high-quality by employing its detecting pattern movements increase proposed method's exploitation potential in last steps. described EO-SCO technique accurately determines PV model unknown parameters. identification these parameters denoted function that must be reduced reducing disparities between estimated experimental data. extensive findings evaluations have confirmed exhibits comparable performance other cutting-edge technologies, particularly relation quality dependability solution. from simulation demonstrate newly optimization methodology yields optimal solutions outperform earlier techniques terms across diverse cell types, while also achieving lowest root mean square error.

Language: Английский

Citations

0