Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules

Zhang, Hongliang; Heidari, Ali Asghar; Wang, Mingjing; Zhang, Lejun; Chen, Huiling; Li, Chengye

doi:10.1016/j.enconman.2020.112764

Cited by 150 publications

(61 citation statements)

References 104 publications

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“…Table 1 illustrates the specifications of these PV modules comprising the key electrical data and thermal coefficients. [49][50][51][52][53][54] On the basis of AEFA explanation and performance, there are two main factors that affect its performance such number of populations N P and the total number of iterations T max . The selected optimal values of these factors are N P = 50 and T max = 500.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…In this matter, two commercial PV modules are used for more verifications like PWP‐201 and STM6‐40 solar units. Table 1 illustrates the specifications of these PV modules comprising the key electrical data and thermal coefficients 49‐54 …”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…The adopted limits of these 9 parameters per each module under study are summarized in Table 2. It is worth mentioning that these limits are same used in the literature to avoid biased comparisons which are extracted from references 49‐57.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

“…To verify the validity of the AEFA‐PV model, its optimal control variables are compared with conventional and recent metaheuristic based ODM and DDM for this module such as GCPSO, 16 TVACPSO, TLBO, GWO and ICA, 17 ISCE, 24 IWOA, 25 FFO, 40 NMSOLMFO, 52 PPSO, 55 HISA, 56 EHA‐NMS, 58 TSLLS, 59 and RF 60 as depicted in Table 3 (It may be useful declaring that the presented values of TDM model parameters are per module). It is a well‐meaning for remarking here that these optimal parameters are very near enough each other.…”

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

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Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

2020

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This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.

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Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

Section: Simulation and Numerical Resultsmentioning

confidence: 99%

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Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

2020

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“…When the algorithm iterates and improves the solution vector continuously, there will always be some solutions that may be closer to the global first-rank solution. As a result, the solution vector obtained by combining orthogonal design with the algorithm can be used as a representative potential sample [51,[57][58][59][60].…”

Section: A Orthogonal Learningmentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the Butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

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An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

Nouri

Aliouat

Naouri

et al. 2023

Int J Communication

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One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a biobjective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings.The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.clients coverage, mesh routers placement, Moth-flame optimization algorithms, network connectivity, wireless mesh networks Recently, the pervasive use of WiFi devices has increased dramatically. The penetration rates of this emerging technology are massive in many deployment areas, thanks to the decreasing prices of wireless devices. A wireless mesh network (WMN) could be formed by hundreds of hotspots covering multiple regions or a single hotspot covering a small area. As illustrated in Figure 1, a generic wireless mesh network consists of mesh routers (MRs) and mesh clients (MCs). In this architecture, mesh routers are static nodes, forming the wireless backbone. Mesh clients are mobile users who can access the network through these routers.

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Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules

Cited by 150 publications

References 104 publications

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

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