Parrot optimizer with multiple search strategies for parameters estimation of proton exchange membrane fuel cells model

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 14, 2025

Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising renewable energy source, generating significant interest in recent years due to their high efficiency, low operating temperature, and durability. Accurately estimating seven unknown parameters the PEMFC electrochemical model is crucial for developing more precise model, thereby improving efficiency performance of systems. For this reason, new optimization method inspired by parrots' (pyrrhura molinaes') behavior, named Parrot Optimizer (PO), introduced here address problem optimal parameter identification ( $$\:{\zeta\:}_{1},\:\:{\zeta\:}_{2},\:{\zeta\:}_{3},\:{\zeta\:}_{4},\:{\lambda\:}_{m},\:{R}_{C},\:\text{a}\text{n}\text{d}\:\beta\:$$ ) models. The estimate these characteristics treated challenging, nonlinear issue that has be addressed with strong technique. paper outlines two improvements basic PO algorithm: first involves employing Opposition-based Learning boost search refine candidate solution generation. second integrates Local Escaping Operator exploration capabilities mitigate risk getting trapped local optima, enhance overall convergence behavior. IPO was rigorously validated through application benchmark functions assess its performance. Three distinct stacks, NedStackPS6, BCS Stack, Ballard Mark V, been used empirically demonstrate efficacy improved optimizing model. Several recognized modeling approaches from literature are comprehensive examination show method's dependability. V units, corresponding SQE values 2.065816 0.012457 0.814325 V. demonstrates 12.87% improvement best measure an 88.37% reduction standard deviation compared PO. results designed approach, including sensitivity analysis, correctly characterizes effectively achieves lowest consistent trajectories.

Язык: Английский

A Novel Lyrebird Optimization Algorithm for Enhanced Generation Rate-Constrained Load Frequency Control in Multi-Area Power Systems with Proportional Integral Derivative Controllers

Processes, Год журнала: 2025, Номер 13(4), С. 949 - 949

Опубликована: Март 23, 2025

This study develops a novel Lyrebird Optimization Algorithm (LOA), technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied handle load frequency control (LFC) optimizing parameters Proportional–Integral–Derivative controller with filter (PIDn). incorporates generation rate constraints (GRCs). The efficiency provided LOA-PIDn evaluated through simulations under various disturbance scenarios and compared against other well-established optimization techniques, including Ziegler–Nichols (ZN), genetic (GA), Bacteria Foraging (BFOA), Firefly Approach (FA), hybridized FA pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. results demonstrate LOA’s ability minimize integral time multiplied absolute error (ITAE) achieve significantly lower settling times for frequencies transferred variances comparison methods. comprehensive inclusion real-world validate LOA as robust effective tool addressing complex challenges modern systems.

Язык: Английский

Photovoltaic Power Generation Forecasting With Bayesian Optimization and Stacked Ensemble Learning

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104950 - 104950

Опубликована: Апрель 1, 2025

Язык: Английский