Turbulent Flow of Water-Based Optimization Using New Objective Function for Parameter Extraction of Six Photovoltaic Models

AbdElminaam, Diaa Salama; Said, Mokhtar; Houssein, Essam H.

doi:10.1109/access.2021.3061529

Cited by 59 publications

(27 citation statements)

References 55 publications

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“…They categorized coronary artery diseases using various classifier forms. This classification was conducted using metaheuristic optimization techniques, such as nature, optimization of particle swarm (PSO) [20], GA [21], Archimedes optimization algorithm (AOA) [22], optimization of chemical reaction (CRO) [23], Henry gas solubility optimization (HGSO) [24], Harris hawks optimization (HHO) [25], [26], Marine Predators Algorithm (MPA) [27] , Barnacles Mating Optimizer (BMO) algorithm [28] , Tunicate Swarm Algorithm (TSA) [29] , Gradient-Based Optimizer (GBO) [30] , Turbulent Flow of Water-Based Optimization (TFWBO) [31] , Owl search algorithm (OSA) [32] , Fitness-Dependent optimizer (FDO) [33] , Squirrel Search Algorithm (SSA) [34] , and sine cosine algorithm (SCA) [35]. In [36], the discrete wavelet transform (DWT) performance and SVM coronary heart diseases, decision tree (DT), K-nearest neighbor, and neural network probability classifiers were compared to identify normal and nonlinear techniques.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

Houssein

AbdElminaam

Ibrahim³

et al. 2021

IEEE Access

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The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life-threatening cardiac arrhythmias. However, the ECG signal is very small, the anti-interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN is proposed to classify the non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques; hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31%, 99.76%, and 99.47% were achieved by the proposed approach on the MIT-BIH,EDB, and INCART databases, respectively.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

Houssein

AbdElminaam

Ibrahim³

et al. 2021

IEEE Access

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“…The techniques that are biology based and employed for the estimation PV parameters are Particle swarm optimization and its variants [40][41][42][43], the Genetic algorithm [44], Differential evolution [45], Artificial bee swarm optimization [46], Artificial bee colony (ABC) optimization [47], the Whale optimization algorithm [48], the Improved ant lion optimizer [49], Biogeography-based optimization [50], the Cuckoo search (CS) algorithm [51], the Bird mating optimization (BMO) algorithm [52], the Flower pollination algorithm [53], the Grey wolf optimizer (GWO) algorithm [54], the Bacterial foraging algorithm [55] and the Slap swarm algorithm [56]. Other interesting meta-heuristics approaches for the extraction of PV parameters are Pattern search [57], the Shuffled complex evolution (SCE) algorithm [58], the Turbulent flow of water algorithm [59,60] and the JAYA algorithm [61,62].…”

Section: Introductionmentioning

confidence: 99%

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

et al. 2021

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Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

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“…Some modifications for these models are also proposed in the literature by adding a series resistance with one diode to represent the quasi-neutral region losses. This modification has been applied to the three models, so were named modified SDM (MSDM), modified DDM (MDDM), and modified TDM (MTDM) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Modified Double-Diode and Triple-Diode Photovoltaic Models Based on Wild Horse Optimizer

et al. 2021

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The increase in industrial and commercial applications of photovoltaic systems (PV) has a significant impact on the increase in interest in studying the improvement of the efficiency of these systems. Estimating the efficiency of PV is considered one of the most important problems facing those in charge of manufacturing these systems, which makes it interesting to many researchers. The difficulty in estimating the efficiency of PV is due to the high non-linear current–voltage characteristics and power–voltage characteristics. In addition, the absence of ample efficiency information in the manufacturers’ datasheets has led to the development of an effective electrical mathematical equivalent model necessary to simulate the PV module. In this paper, an application for an optimization algorithm named Wild Horse Optimizer (WHO) is proposed to extract the parameters of a double-diode PV model (DDM), modified double-diode PV model (MDDM), triple-diode PV model (TDM), and modified triple-diode PV model (MTDM). This study focuses on two main objectives. The first concerns comparing the original models (DDM and TDM) and their modification (MDDM and MTDM). The second concerns the algorithm behavior with the optimization problem and comparing this behavior with other recent algorithms. The evaluation process uses different methods, such as Root Mean Square Error (RMSE) for accuracy and statistical analysis for robustness. Based on the results obtained by the WHO, the estimated parameters using the WHO are more accurate than those obtained by the other studied optimization algorithms; furthermore, the MDDM and MTDM modifications enhanced the original DDM and TDM efficiencies.

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Turbulent Flow of Water-Based Optimization Using New Objective Function for Parameter Extraction of Six Photovoltaic Models

Cited by 59 publications

References 55 publications

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Parameter Estimation of Modified Double-Diode and Triple-Diode Photovoltaic Models Based on Wild Horse Optimizer

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