A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems DOI Open Access
Hegazy Rezk, A.G. Olabi,

Tabbi Wilberforce

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(7), P. 5732 - 5732

Published: March 24, 2023

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due the operating conditions. Since model’s accuracy is required for establishing proper control and management plans, identifying a critical prominent task. Various techniques have been developed identify these parameters. However, metaheuristic algorithms received much attention use in tackling wide range of optimization issues relating parameter extraction. This work provides an exhaustive literature review on solving extraction utilizing recently algorithms. paper includes newly published articles each studied context its discussion. It aims approve applicability make understanding deployment easier. there not any exact that can offer satisfactory performance all issues, especially problems large search space dimensions. As result, capable searching very spaces possible solutions thoroughly investigated review. Furthermore, depending behavior, divided into four types. These types details included this paper. Then, basics identification process presented discussed. Fuel cells, electrochemical batteries, photovoltaic panel analyzed.

Language: Английский

Dandelion Optimizer algorithm-based method for accurate photovoltaic model parameter identification DOI Creative Commons
Abdelfattah Elhammoudy, Mustapha Elyaqouti,

El Hanafi Arjdal

et al.

Energy Conversion and Management X, Journal Year: 2023, Volume and Issue: 19, P. 100405 - 100405

Published: June 9, 2023

The utilization of photovoltaic (PV) energy has experienced a significant surge in the last few decades, resulting rise research endeavours to comprehend its workings better. One focal points this is electrical modelling PV cells and modules. Several equivalent circuits have been proposed model them, such as single-diode (SDM), double-diode (DDM), triple-diode (TDM). main challenge identifying optimal circuit parameters. This study introduces novel method based on metaheuristic algorithm named Dandelion Optimizer (DO) coupled with numerical Newton-Raphson (NR) estimate Various models, including (SDM) were utilized by (DONR) determine parameters six different modules, RTC France, Photowatt-PWP201, STP6-120/36. A comparative analysis was conducted ten other widely recognized methods demonstrate effectiveness method. results that more accurate estimating than methods. According experimental results, superior accurately terms accuracy, reliability, convergence. Specifically, root mean squared error values obtained using (SDM, DDM) for PWP201, STP6-120/36 are (7.73939E-04, 7.56515E-04), (2.08116E-03, 2.07842E-03) (1.42575E-02, 1.45952E-02), respectively.

Language: Английский

Citations

34
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

et al.

Energy Reports, Journal Year: 2023, Volume and Issue: 9, P. 4654 - 4681

Published: March 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.

Language: Английский

Citations

30
Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers DOI Creative Commons
Mahmoud A. El‐Dabah, Mohamed H. Hassan, Salah Kamel

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 27168 - 27188

Published: Jan. 1, 2023

This work discusses the production of a novel hybrid algorithm by combining gorilla troops optimizer (GTO) with gradient-based optimizers (GBO) approach. The approach is called GTO-GBO, it offered as useful tool for optimizing power system stabilizer (PSS) used in IEEE four-generator, two-area multi-machine subjected to three-phase short-circuit fault. MATLAB/Simulink software was utilized carry out assessments. suggested initially evaluated using multiple benchmark functions unimodal and multimodal properties. results are then compared other competing algorithms (artificial ecosystem optimizer, artificial rabbits Coati Optimization Algorithm, northern goshawk optimization). comparisons various reveal developed GTO-GBO algorithm's considerable promise. demonstrates improved balance global local search stages. proposed performance also developing an optimum performing PSS further examination, allowing observation its capabilities difficult real-world engineering challenges. To illustrate applicability superior such complicated problem, damping controller formulated optimization optimal parameters. latter case's findings competitive where efficiency robustness this enhance stability.

Language: Английский

Citations

27
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

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 232, P. 120827 - 120827

Published: June 16, 2023

Language: Английский

Citations

24
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
Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm DOI Creative Commons

T. Zhou,

Chao Shang

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 1422 - 1445

Published: Jan. 9, 2024

Abstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics PV cells. The goal this paper propose multi strategy sine–cosine algorithm (SCA), named enhanced (ESCA), evaluate nondirectly measurable parameters ESCA introduces concept population average position increase exploration ability, at same time personal destination agent mutation mechanism competitive selection into SCA provide more search directions for while ensuring accuracy diversity maintenance. To prove that proposed best choice extracting cells, evaluated by single‐diode model, double‐diode three‐diode module model (PVM), compared with eight existing popular methods. Experimental results show outperforms similar methods in terms maintenance, high efficiency, stability. In particular, method less than 0.081, 0.144, 0.578 standard deviation statistics metrics three PVM models (PV‐PWP201, STM6‐40/36, STP6‐120/36), respectively. Therefore, an accurate reliable

Language: Английский

Citations

12
Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem DOI Open Access

Sultan Hassan Hakmi,

Hashim Alnami,

Ghareeb Moustafa

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(9), P. 1611 - 1611

Published: April 23, 2024

A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize physical phenomenon of rime-ice growth. It simulates hard-rime and soft-rime processes, constructing mechanisms puncture search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation PDLO introduces non-linearities enhancing its adaptability, convergence speed, global search capability compared conventional approach. proposed MRIME algorithm designed identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including One-Diode Model (ONE-DM) Two-Diode TWO-DM, determine unspecified parameters PV. approach method using two commercial PV modules, namely STM6-40/36 R.T.C. France cell. simulation results are juxtaposed with those from contemporary algorithms based on published research. outcomes related also in relation various existing studies. indicate that demonstrates substantial improvement rates for cell, achieving 1.16% 18.45% ONE-DM, respectively. For it shows significant reaching 1.14% 50.42%, comparison previously results, establishes superiority robustness.

Language: Английский

Citations

11
A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain DOI Creative Commons
Hanmi Zhou,

Linshuang Ma,

Xiaoli Niu

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 296, P. 108807 - 108807

Published: April 2, 2024

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with extreme gradient boosting (XGBoost) model to propose novel NGO-XGBoost model. performance was evaluated using meteorological data from 30 stations North China Plain and compared XGBoost, random forest (RF), k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method results RF, adaptive (AdaBoost), categorical (CatBoost) models used obtain importance factors estimating ETo, thereby determine optimal combination inputs indicated that by top 3, 4, 5 important as input combinations, all achieved high estimation accuracy. It worth noting there were significant spatial differences precisions four models, but exhibited consistently precisions, global indicator (GPI) rankings 1st, range coefficient determination (R2), nash efficiency (NSE), root mean square error (RMSE), absolute (MAE) bias (MBE) 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 −0.254–0.062 respectively. Furthermore, accuracy varied across different seasons, which more significantly affected humidity wind speed winter. When target station insufficient, trained historical neighboring still maintained precision. Overall, recommends reliable for provides calculating absence data.

Language: Английский

Citations

10
Improved crayfish optimization algorithm for parameters estimation of photovoltaic models DOI

Lakhdar Chaib,

Mohammed Tadj, Abdelghani Choucha

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 313, P. 118627 - 118627

Published: June 1, 2024

Language: Английский

Citations

10
Combining the mRMR technique with the Northern Goshawk Algorithm (NGHA) to choose genes for cancer classification DOI
Abrar Yaqoob

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: May 7, 2024

Language: Английский

Citations

9