Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models

Jiao, Shan; Chong, Guoshuang; Huang, Changcheng; Hu, Hanqing; Wang, Mingjing; Heidari, Ali Asghar; Chen, Huiling; Zhao, Xuehua

doi:10.1016/j.energy.2020.117804

Cited by 200 publications

(77 citation statements)

References 78 publications

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“…(PHOTOVOLTAIC MODULE MODEL) Fotovoltaik sistemlerin doğrusal olmayan karakteristiklerini belirlemek, özellikle matematiksel modeli doğru bir şekilde tanımlanmasında önemlidir. Fotovoltaik modül devreye seri veya paralel olarak bağlanmış çoklu güneş pillerinden oluşmaktadır [7]. Tek veya çift diyot modellerine dayanan FV modül modelleri diyotların seri ve paralel yerleştirilmesine izin veren Ndiyotlu FV modeline genişletilmiştir [9].…”

Section: Fotovoltai̇k Modül Modeli̇unclassified

“…Son olarak meta-sezgisel algoritmalarda amaç fonksiyonunun esnek kullanımı ve kolay uygulanabilir olma özelliği sayesinde analitik ve deterministik yöntemlerde karşılaşılan sorunların üstesinden etkin bir şekilde gelmektedir. Son zamanlarda, farksal gelişim (FG) [6], parçacık sürü optimizasyon (PSO) [5], Geliştirilmiş Harris Şahini Optimizasyonu (EHHO) [7], Geliştirilmiş Adaptif Farksal Gelişim (EJADE) [1], Geliştirilmiş JAYA [4], Memetik uyarlanabilir farksal gelişim (MADE) [8], Rcr-IJADE [9], Kuş Çiftleşme Optimizeri (BMO) [10], Jade tabanlı gibi FV modellerinin parametre tahmini için meta-sezgisel yöntemler kullanılmıştır. Meta-sezgisel algoritmalar problemlerin çözümünde etkili ve tatmin edici sonuçlar vermesine rağmen bazı dezavantajlara da sahiptir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…tarafından ise Harris şahin optimizasyon algoritmasına ortogonal öğrenme ve genel karşıtlık temelli öğrenme eklemişlerdir. Her iki yöntemde de parameterlerin bulunması verimli ve etkili bir şekilde sağlanmıştır [7]. [13][14][15].…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…Bu testlerde anlamlılık değeri [16], etki büyüklüğü [17] ve istatistiksel güç [18] değerleri incelenerek ve elde edilen sonuçlar karşılaştırılmıştır. Kaotik haritaların performansını deneysel olarak karşılaştırmak için literatürde bulunan Geliştirilmiş Harris Şahini Optimizasyonu (EHHO) [7], Geliştirilmiş Adaptif Farksal Gelişim (EJADE) [1], Geliştirilmiş JAYA [4], Memetik uyarlanabilir farksal gelişim (MADE) [8], Farksal Gelişim (FG) [9], Kuş Çiftleşme Optimizasyon Algoritması (BMO) [10], Jade tabanlı Rcr-IJADE [9] algoritmaları seçilmiştir. Bu algoritmaların seçilmesinin nedeni, hibrit algoritma olarak çok iyi performans göstermeleridir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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Fotovoltaik Modellerin Parametre Çıkarımı İçin Geliştirilmiş Bir Kaotik Tabanlı Balina Optimizasyon Algoritması

Garip¹,

Çimen

Boz³

2022

Politeknik Dergisi

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Section: Fotovoltai̇k Modül Modeli̇unclassified

Section: Gi̇ri̇ş (Introduction)unclassified

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Fotovoltaik Modellerin Parametre Çıkarımı İçin Geliştirilmiş Bir Kaotik Tabanlı Balina Optimizasyon Algoritması

Garip¹,

Çimen

Boz³

2022

Politeknik Dergisi

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“…As the latest member of MEAs, HHO performs a simple mechanism and can be used easily. Since proposed [53], it has been used widely [54]- [56], such as solar energy [57]- [59], feature selection [60], drug design and discovery [61]. Furthermore, a large number of improved HHO variants have been presented, for example, hybrid HHO-based sine cosine mechanism [62], Nelder-mead driven HHO [63], generalized Gaussian distribution HHO [64], multi-objective HHO [65], mutation strategies-based HHO [66], diversification enriched HHO [58], Multi-population version [67] random forest model based-HHO [68].…”

Section: Proposed Sglhhomentioning

confidence: 99%

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

et al. 2020

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Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

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Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Heidari

Kuang

et al. 2022

Intl J of Energy Research

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Summary The inherent multimodal and nonlinear characteristics of solar photovoltaic (PV) systems make it challenging to accurately extract PV parameters. Therefore, this study proposes an efficient and accurate optimization technique, improved Hunger Games Search, to identify unknown parameters in PV systems named IHGS. IHGS makes full use of the population information generated by the differential vectors while incorporating the quantum rotation gate strategy and the Nelder‐Mead simplex method (NMs) are fused to further increase the population diversity while rotating the corresponding positions of the search agents, thus efficiently exploring the neighborhood of the optimal solution in the decision space and improving the stability of the algorithm in developing the global optimal solution. The first part of the experiments is to simply verify the effectiveness and the stable merit‐seeking ability of the IHGS to handle complex functions by putting the proposed IHGS to test on the IEEE CEC 2017 benchmark test suite. The efficiency of IHGS is verified based on Wilcoxon singed‐rank test and Friedman tests. Next, we apply IHGS to three different PV module models (single‐diode, double‐diode, and PV module) for PV parameter extraction experiments to verify the efficiency and accuracy of IHGS in extracting PV parameters. The root mean square error produced by the IHGS proposed in this study is 9.8602E‐04, 9.8248E‐04, and 2.4251E‐03 for SDM, DDM, and PV modules, respectively. Examples show that IHGS outperforms HGS and other advanced algorithms in terms of convergence speed, data fitting, consistency, and stability. Finally, we perform the PV module parameter extraction problem on three commercial PV models (thin‐film ST40, monocrystalline SM55, and multicrystalline KC200GT). We observe that IHGS can still maintain high accuracy when dealing with commercial PV models under complex environments such as different irradiation and temperature. The results show that IHGS is a valuable optimization technique for solar PV parameter estimation based on multi‐model prediction.

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Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models

Cited by 200 publications

References 78 publications

Fotovoltaik Modellerin Parametre Çıkarımı İçin Geliştirilmiş Bir Kaotik Tabanlı Balina Optimizasyon Algoritması

Fotovoltaik Modellerin Parametre Çıkarımı İçin Geliştirilmiş Bir Kaotik Tabanlı Balina Optimizasyon Algoritması

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

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