Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126186 - 126186
Published: Dec. 1, 2024
Language: Английский
Electrical Engineering, Journal Year: 2024, Volume and Issue: 106(5), P. 6565 - 6585
Published: April 20, 2024
Language: Английский
Citations
15
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
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124882 - 124882
Published: July 29, 2024
Language: Английский
Citations
14
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
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
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
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
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
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
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