Whale optimisation algorithm for photovoltaic model identification

Elazab, Omnia S.; Hasanien, Hany M.; Elgendy, Mohammed A.; Abdeen, Amr M.

doi:10.1049/joe.2017.0662

Cited by 37 publications

(20 citation statements)

References 22 publications

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“…The whale optimization algorithm (WOA) is influenced by the natural characteristics of the underwater movement of whales [30]. The technical optimization steps involved in WOA are given in the following description, which can be divided into two major phases, namely exploitation and exploration phases.…”

Section: Adapted Whale Optimization For Underwater Networkmentioning

confidence: 99%

“…To address the issue in geolocation-centric relay node selection, void avoidance approach has been investigated, utilizing the quality of service-oriented underwater backtracking [29]. The aforementioned underwater relay node optimization techniques majorly rely on either geolocation-centric node selection or the quality of service-centric node selection, without considering the dynamic self-mobility of the medium of communication in underwater environments, such as in whale optimization [30].In this context, this paper proposes an adapted whale optimization algorithm-based energy and a delay-centric green UWSNs framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection.…”

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confidence: 99%

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confidence: 99%

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W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Rathore

Sangwan

Mazumdar

et al. 2020

Sensors

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Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.Sensors 2020, 20, 1377 2 of 23 water-based transport applications [6,7], oil, and natural gas production applications [8,9], and developing fishing-centric industries [10,11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13]. Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14][15][16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17][18][19].Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20,21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment o...

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Section: Adapted Whale Optimization For Underwater Networkmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Rathore

Sangwan

Mazumdar

et al. 2020

Sensors

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“…Their main advantage is that they do not need continuity and differentiability of the objective function In the last decade, metaheuristics have been frequently applied for parameter estimation of circuit model parameters of solar PV cells. The main develops in recent research are: genetic algorithm (GA) [25], grey wolf optimization (GWO) [26], particles swarm optimization (PSO) [27], moth-flame optimization algorithm (MFOA) [28], harmony search (HS) [29], artificial neural network (ANN) [30], multi-verse optimizer (MVO) [31], bond-graph based modelling [32], cuckoo search (CS) [33], bacterial foraging optimization [34], multiple learning backtracking search algorithm (MLBSA) [35], whale optimization algorithm (WAO) [36], salp swarm-inspired algorithm (SSA) [37]… New metaheuristic algorithms have been also recently developed to solve mathematic and engineering problems. [38] used World Cup Optimization (WCO) algorithm to find the optimal parameters of PID controller; in [39] a new algorithm based on Variance Reduction of Guassian Distribution is proposed; a new algorithm based on the invasive weed by the quantum computing is proposed by [40]; [41] combined Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to train wavelet neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm has been applied to extract the best parameters of a PV cell and module under uniform and partial shading conditions. Five recent algorithms (SSA [37], GWO, MFOA [28], WAO [36], MVO [31]) are also implemented on the same computer with the parameters gave by authors. The result obtained from the EVPS is compared with other recent methods in the literature and different results obtain to demonstrate the high quality of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

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To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.weather conditions change, analytical methods become ineffective [18].Numerical extraction techniques based on some algorithm fit the points on the PV characteristic curve. Compared to the analytical method, an accurate result can be attained since the algorithm tries to consider all points on the characteristic curve [19]. In the literature, the Newton-Raphson method is the most used [19] [20] [21]; In [22], a numerical method is proposed for modeling and the simulation of PV. The method finds the five parameters from the current-voltage characteristic by using three points of the curve (maximum power, open circuit and short circuit). In [23], the Levenberg Marquardt algorithm is

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Different diode models comparison using Lambert W function for extracting maximum power fromBIPVmodules

2020

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Perfect modeling of the building-integrated photovoltaic (BIPV) module circuit equivalent is required for examining the operation of a BIPV system. Before designing the power electronic converters of an overall BIPV system, a perfect diode modeling is required that usually resembles the I-V and P-V characteristics of the BIPV modules. In this research article, different types of diode modeling of BIPV systems along with their comparative analysis based on the Lambert W function in MATLAB/Simulink environment is presented. The main aim of this research article is to analyze Mprime transparent M 115-130P-FI BIPV modules (115 Wp and 130 Wp) and compare the existing diode models in terms of accuracy and extraction of unknown parameters for the same. Simulation results for Lambert W function based comparison of the five-parameter model (FPM), seven-parameter model (SPM) and nineparameter model (NPM) power of 115 Wp and 130 Wp BIPV modules along with their percentage errors are well presented. Lambert W function based comparison of FPM, SPM, and NPM is further made at different values of irradiations and temperatures respectively. K E Y W O R D S building-integrated photovoltaic (BIPV), five-parameter model (FPM), Lambert W function, maximum power, nine-parameter model (NPM), seven-parameter model (SPM)

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Whale optimisation algorithm for photovoltaic model identification

Cited by 37 publications

References 22 publications

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Different diode models comparison using Lambert W function for extracting maximum power fromBIPVmodules

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