Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm

Pourmousa, Nafiseh; Ebrahimi, Shayan; Malekzadeh, Milad; Alizadeh, Mojtaba

doi:10.1016/j.solener.2019.01.026

Cited by 97 publications

(31 citation statements)

References 43 publications

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“…Number of cells in parallel [61] 7.7301E − 04 NA NA NA LI [62] 1.0548E − 03 NA NA NA TSLLS [63] 7.7301E − 04 NA NA NA Tong and Pora [49] 1.5051E − 03 NA NA NA Tayyan [64] 2.9117E − 03 NA NA NA MABC [65] 9.862E − 04 NA NA NA ABSO [66] 9.9124E − 04 NA NA NA BBO-M [67] 9.8634E − 04 NA NA NA GGHS [68] 9.9078E − 04 NA NA NA CARO [69] 9.8665E − 04 NA NA NA SOS [24] 9.8609E − 04 1.1982E − 03 1.0245E − 5.2184E − 05 MSSO [70] 9.8607E − 04 NA NA NA CWOA [21] 9.8604E − 04 NA NA 1.0216E − 08 CSO [71] 9.8602E − 04 NA 9.8602E − 5.4941E − 09 MADE [72] 9.8602E − 04 9.8602E − 04 9.8602E − 2.74E − 15 EO-Jaya [73] 9.8603E − 04 NA NA NA ILCOA [74] 9.8602E − 04 NA NA NA FPSO [75] 9.8602E − 04 NA NA 2.0142E − 08 PGJAYA [76] 9.8602E − 04 9.8602E − 04 9.8602E − 1.4485E − 09 OBWOA [77] 9.8602E − 04 NA NA NA ABC-TRR [78] 9.8602E − 04 9.8602E − 04 9.8602E − 6.15E − 17 NM-MPSO [79] 9.8602E − 04 NA NA NA SDO 9.8602E − 04 9.8616E − 04 9.8603E − 2.5141E − 08 DDM MABC [65] 9.8276E − 04 NA NA NA ABSO [66] 9.8344E − 04 NA NA NA BBO-M [67] 9.8272E − 04 NA NA NA IGHS [68] 9.8635E − 04 NA NA NA CARO [69] 9.8260E − 04 NA NA NA SOS [24] 9.8518E − 04 1.3498E − 03 1.0627E − 9.6141E − 05 MSSO [70] 9.8281E − 04 NA NA NA CWOA [21] 9.8279E − 04 NA NA 1.1333E − 07 CSO [71] 9.8252E − 04 NA 9.9619E − 3.4681E − 05 MADE [72] 9.8261E − 04 9.8786E − 04 9.8608E − 8.02E − 05 EO-Jaya [73] 9.8262E − 04 NA NA NA ILCOA [74] 9.8257E − 04 NA NA NA FPSO [75] 9.8253E − 04 NA NA 3.1469E − 08 PGJAYA…”

Section: Nomenclature I Dmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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Section: Nomenclature I Dmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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“…Firstly, to validate the performance of the proposed G-WOA algorithm in learning ontology from Arabic text, we compared the solution results returned by it to those returned by the ordinary GA and WOA. Moreover, extensive comparisons were conducted by comparing the performance of the G-WOA algorithm to three other bio-inspired algorithms: PSO [44], moth flame optimization (MFO) [45], and the hybrid differential evolution-whale optimization (DE-WOA) [46]. To compare these bio-inspired algorithms, the parameter setting had to be determined for each.…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

“…Although the DE algorithm had robust global searchability, it was weak in the exploitation, and converged slowly. Thus, the DE algorithm needs to be optimized for it to be hybridized with other algorithms, as reported in [46]. Thus, the DE-WOA has the third rank in terms of the Arabic ontology learning.…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

“…Thirdly, investigating the comparable performance between the proposed G-WOA and five other bio-inspired optimization algorithms [32,39,[44][45][46], when learning ontology from Arabic text, where its solutions are also compared to those obtained by the other algorithms, across different Arabic corpora. To the best of our knowledge, the proposed and compared bio-inspired algorithms have not been investigated in Arabic or non-Arabic ontology learning yet.…”

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confidence: 99%

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A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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“…One of the most widely used converters in this field is DC-DC boost converter that is used to regulate the output voltage of systems requiring higher voltage levels. Therefore, this converter is applied in renewable energy sources such as solar energy [3], fuel cell [4] and hybrid vehicles [5]. So, designing an appropriate control scheme that leads to enhance the voltage regulation quality is one of the most important problems in industry.…”

Section: Introductionmentioning

confidence: 99%

An immersion and invariance based input voltage and resistive load observer for DC–DC boost converter

2019

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In this paper, a new nonlinear observer is presented for DC-DC boost converter using immersion and invariance (I&I) technique. The proposed nonlinear observer is easy to implement, simple to tune and realizes global exponential convergence of the estimation error to zero. It is assumed that the input voltage and output load of converter are unavailable and the I&I observer is designed to estimate unavailable parameters using output voltage and inductor current information. Considering optimal performance of observer, an improved particle swarm optimization algorithm is employed to determine observer gains. The search space of parameters in the proposed optimization algorithm is modified and according to this modification, the weak parameters is eliminated and the new parameters are defined for optimization process instead of them. In order to validate the effectiveness of the proposed observer, the practical test is implemented and the experimental results confirm the efficiency of the proposed I&I observer.

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Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm

Cited by 97 publications

References 43 publications

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

An immersion and invariance based input voltage and resistive load observer for DC–DC boost converter

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