Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy

Yeşilbudak, Mehmet

doi:10.3390/en14185735

Cited by 24 publications

(10 citation statements)

References 73 publications

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“…To keep the balance between local and global search, the DLH algorithm is introduced to optimize the GWO algorithm. The inspiration for DLH comes from the hunting behavior of individuals in nature [39]. In the GWO algorithm, the concept of neighborhood is introduced to provide a new choice of candidate location for every gray wolf.…”

Section: Improved Gwo Algorithm Based On Dlhmentioning

confidence: 99%

“…The number range of neurons in the hidden layer of BPNN is determined according to the empirical Formula (39).…”

Section: Parameter Selection Of Bpnn (1) Selection Of Neuron Numbersmentioning

confidence: 99%

“…According to the empirical formula (39), the number range of neurons in the hidden layer is within [4,14]. Different numbers of neurons are set, respectively, and experiments are conducted to obtain the corresponding average accuracy.…”

Section: Selection Of Neuron Numbersmentioning

confidence: 99%

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Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

et al. 2023

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The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation.

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Section: Improved Gwo Algorithm Based On Dlhmentioning

confidence: 99%

“…The number range of neurons in the hidden layer of BPNN is determined according to the empirical Formula (39).…”

Section: Parameter Selection Of Bpnn (1) Selection Of Neuron Numbersmentioning

confidence: 99%

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Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

et al. 2023

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“…However, the parameter settings are complicated and searching speed is slow; furthermore, the convergence property is pretty poor. In order to further strike a balance between the search range and the convergence accuracy in optimization algorithms, a series of bionic intelligent optimization algorithms, such as gray wolf optimization algorithm [12] (GWO), artificial bee colony algorithm [13] (ABC), and the bacterial foraging algorithm [14] (BFA), have been proposed in recent years. Sparrow search algorithm (SSA) [15] is a novel swarm intelligence optimization algorithm that is inspired by foraging and antipredation behaviors of sparrows.…”

Section: Introductionmentioning

confidence: 99%

FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables

Sun

Meng

Dong

2022

Computational Intelligence and Neuroscience

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In order to enhance the classification accuracy and the generalization performance of the SVM classifier in cable partial discharge (PD) pattern recognition, a firefly optimized sparrow search algorithm (FoSSA) is proposed to optimize its kernel function parameters and penalty factors. First, the Circle-Gauss hybrid mapping model is employed in the population initialization stage of the sparrow search algorithm (SSA) to eliminate the uneven population distribution of random mapping. Sparrows tend to fall into local extremums during the search process, while the firefly algorithm has a fast optimization speed and strong local search ability. Thus, a firefly disturbance is added in the sparrow search process, and the fitness value is recalculated to update the sparrow position to enhance the sparrow’s local optimization ability and accuracy. Finally, based on the SSA, a dynamic step-size strategy is adopted to make the step size dynamically decrease with the number of iterations and improve the accuracy of convergence. Six benchmark functions are employed to evaluate the optimization performance of the FoSSA quantitatively. Experiment results show that the recognition accuracy of the PD patterns using the SVM optimized by the FoSSA could reach 97.5%.

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“…To minimize the probability of the Harris hawks optimization (HHO) getting stuck in local optima,Naeijian et al, (2021) added a strategy that eliminates the worst solutions and generates new solutions in the search space. However, the improved algorithm, whippy HHO (WHHO), requires two new control parameters Yesilbudak et al, (2021). used a grey wolf optimizer with a dimension learning-based hunting search strategy (I-GWO) to mitigate the imbalance between intensification and diversification mechanisms and the lack of population diversity.…”

mentioning

confidence: 99%

Bypass diode effect and photovoltaic parameter estimation under partial shading using a hill climbing neural network algorithm

et al. 2022

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In recent decades, population growth and industrial evolution have led to a significant increase in the need to produce electricity. Photovoltaic energy has assumed a key role in responding to this need, mainly due to its low cost and reduced environmental impact. Therefore, predicting and controlling photovoltaic power is an indispensable task nowadays. This paper studies how photovoltaic power can be affected under non-uniform irradiance conditions, i.e., when the photovoltaic energy production system is under partial shading. Concretely, the effect of bypass diodes on the current-voltage characteristic curve, according to the shaded area, was studied and the power loss under partial shading was quantified. In addition, electrical characteristics and the temperature distribution in the photovoltaic module were analyzed. Furthermore, we propose a hill climbing neural network algorithm to precisely estimate the parameters of the single-diode and double-diode models under partial shading conditions and, consequently, predict the photovoltaic power output. Different shading scenarios in an outdoor photovoltaic system were created to experimentally study how partial shading of a photovoltaic module affects the current-voltage characteristic curve. Six shading patterns of a single cell were examined, as well as three shading patterns of cells located in one or more strings. The hill climbing neural network algorithm was experimentally validated with standard datasets and different shading scenarios. The results show that the hill climbing neural network algorithm can find highly accurate solutions with low computational cost and high reliability. The statistical analysis of the results demonstrates that the proposed approach has an excellent performance and can be a promising method in estimating the photovoltaic model parameters under partial shading conditions.

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Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy

Cited by 24 publications

References 73 publications

Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

Study on Downhole Geomagnetic Suitability Problems Based on Improved Back Propagation Neural Network

FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables

Bypass diode effect and photovoltaic parameter estimation under partial shading using a hill climbing neural network algorithm

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