A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

Ridha, Hussein Mohammed; Hizam, Hashim; Mirjalili, Seyedali; Othman, Mohammad Lutfi; Ya’acob, Mohammad Effendy; Abualigah, Laith

doi:10.1109/access.2022.3142779

Cited by 34 publications

(13 citation statements)

References 119 publications

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“…Likewise, Gupta et al [128] used four strategies on the HHO namely: proposing a new nonlinear parameter for the prey energy, greedy selection, different rapid dives, and OBL. Other studies that used optimization methods to solve the parameter extraction problems refer to [129][130][131].…”

Section: Opposite Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Opposite Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…During the first step, the algorithm performs a global search of the solution space, while the second step focuses on finding the most promising areas. The WOA algorithm simulates the behavior of humpback whales, striving to achieve the best possible performance [12]. After WOA, in 2014 the researcher Mirjalili developed the meta-analytical named GWO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved power quality based PVDG system using different optimised MPPT controllers

Tiwari

2024

Phys. Scr.

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The characteristic curve of photovoltaic (PV) modules is nonlinear due to environmental influences. The maximum quantity of energy can only be taken from the nonlinear angle of the solar system at one exact moment. The primary function of an MPPT (maximum power point tracking) controller in a PV system is to extract maximum power point (MPP) from solar panels. In this paper, an enhanced MPPT approach using different optimization techniques is suggested for PV systems. The Fuzzy logic controller (FLC), Whale Optimization Algorithm (WOA), and modified Grey Wolf Optimization Algorithm (MGWO) are presented for the MPPT controller. Among all methods, the MGWO technique is one of the best at determining the MPP in solar systems because of its quick response and low fluctuations. Accurate training data is one of the biggest challenges in developing an effective MGWO. The irradiance and temperature are taken into consideration as input variables, and the optimal voltages are optimized as an output variable using the MGWO algorithm. Matlab/Simulink simulations are run to verify the suggested model’s suggested tracking effectiveness. To ensure accurate results, simulations are run under various climatic conditions. The simulation result shows that the proposed method operates correctly in a variety of climatic conditions efficiently.

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“…Mirjalili (2015), for example, harnessed the power of the anti-lion optimizer (ALO) to address design challenges in structures like cantilever beams, 3-bar trusses and gear trains. These metaheuristic algorithms can be further categorized into subgroups, including swarm intelligence, evolutionary algorithms, physical algorithms, and hybrid algorithms (Abdel-Basset et al ., 2018; Khairan et al ., 2022; Ridha et al ., 2022). In the structural design, problems like the welded beam design have been tackled using genetic algorithms (GA), particle swarm optimization (PSO), teaching–learning based optimization (TLBO) and other techniques (Abdel-Basset et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In the structural design, problems like the welded beam design have been tackled using genetic algorithms (GA), particle swarm optimization (PSO), teaching–learning based optimization (TLBO) and other techniques (Abdel-Basset et al ., 2018). When it comes to parameter extraction for photovoltaic models under changing climate conditions, the hybrid arithmetic operations algorithm (HAOA) based on Newton–Raphson has been applied (Ridha et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

Prasanth Kumar,

Boggarapu,

Narayana Murty

2023

MMMS

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PurposeThis paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.Design/methodology/approachIn order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.FindingsFinally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.Practical implicationsBased on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.Originality/valueEven though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

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A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

Cited by 34 publications

References 119 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Improved power quality based PVDG system using different optimised MPPT controllers

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

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