scholarly journals Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 358 ◽  
Author(s):  
Efstratios Batzelis
Keyword(s):  
Extraction Method ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Comparative Assessment ◽  
Research Topic ◽  
Model Parameters ◽  
Assessment Criterion ◽  
Straightforward Calculation ◽  
Photovoltaic Modules ◽  
First Time

The extraction of the photovoltaic (PV) model parameters remains to this day a long-standing and popular research topic. Numerous methods are available in the literature, widely differing in accuracy, complexity, applicability, and their very nature. This paper focuses on the class of non-iterative parameter extraction methods and is limited to the single-diode PV model. These approaches consist of a few straightforward calculation steps that do not involve iterations; they are generally simple and easy to implement but exhibit moderate accuracy. Seventeen such methods are reviewed, implemented, and evaluated on a dataset of more than one million measured I-V curves of six different PV technologies provided by the National Renewable Energy Laboratories (NREL). A comprehensive comparative assessment takes place to evaluate these alternatives in terms of accuracy, robustness, calculation cost, and applicability to different PV technologies. For the first time, the irregularities found in the extracted parameters (negative or complex values) and the execution failures of these methods are recorded and are used as an assessment criterion. This comprehensive and up-to-date literature review will serve as a useful tool for researchers and engineers in selecting the appropriate parameter extraction method for their application.

scholarly journals Are streamflow recession characteristics really characteristic?

2013 ◽  
Vol 17 (2) ◽  
pp. 817-828 ◽  
Author(s):  
M. Stoelzle ◽  
K. Stahl ◽  
M. Weiler
Keyword(s):  
Extraction Method ◽  
Storage Capacity ◽  
Extraction Methods ◽  
Model Parameters ◽  
Catchment Scale ◽  
Model Parameterization ◽  
Recession Curves ◽  
Recession Analysis ◽  
Meso Scale

Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storage–outflow relationship based on the dQ/dt−Q method. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how comparably the resulting recession models distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage–outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 meso-scale catchments in Germany. We found plausible ranges for model parameterization; however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method, not all rank the catchments consistently, and the differences among some of the methods are larger than among the catchments. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of the storage–outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple-methods approach to investigating streamflow recession characteristics should be considered for applications whenever possible.

MOSFET MODELING AND PARAMETER EXTRACTION FOR RF IC'S

2001 ◽  
Vol 11 (04) ◽  
pp. 953-1006 ◽  
Author(s):  
MINKYU JE ◽  
ICKJIN KWON ◽  
HYUNGCHEOL SHIN ◽  
KWYRO LEE
Keyword(s):  
High Frequency ◽  
Circuit Simulation ◽  
Low Frequency ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Model Parameters ◽  
Large Signal ◽  
Charge Conservation ◽  
Macro Modeling ◽  
High Frequency Effects

After reviewing the basic concept and general strategies, we have examined a variety of examples of modeling and parameter extraction methods for RF MOSFET's. Modeling and parameter extraction techniques popular in III-V FET modeling were reviewed and recent efforts to model the RF MOSFET and extract the model parameters were examined in light of the differences between the MOSFET and the III-V FET. A very simple and accurate parameter extraction method studied in our laboratory for three-terminal modeling considering charge conservation is also introduced. Our works have two important implications. One is that the consideration for charge conservation is important not only for accurate device modeling and circuit simulation but even more for proper parameter extraction. Another is that one accurate large-signal I-V model is enough to be used for DC, low-frequency analog, as well as RF circuit simulation. Four-terminal modeling based on new equivalent circuits to address the high-frequency effects arising in a MOSFET is very complicated and not practical for CAD applications, even without considering the substrate coupling terms. As a temporary alternative, the macro-modeling approach is examined with various examples.

scholarly journals Research on Parameter Extraction Method of Photovoltaic Module Based on Improved Hybrid Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Haitao Wu ◽  
Zhou Shang
Keyword(s):  
Differential Evolution ◽  
Equivalent Circuit ◽  
Hybrid Algorithm ◽  
Equivalent Circuit Model ◽  
System Optimization ◽  
Parameter Extraction ◽  
Circuit Model ◽  
Photovoltaic Module ◽  
Model Parameters ◽  
Photovoltaic Modules

Correct extraction of the equivalent circuit model parameters of photovoltaic modules is of great significance for power prediction, fault diagnosis, and system optimization of photovoltaic power generation systems. Although there are many methods developed to extract the equivalent circuit model parameters of the photovoltaic module, it is still challenging to ensure the stability and operational efficiency of the extract method. In order to effectively extract the parameters of photovoltaic modules, this paper proposes a hybrid algorithm combining analytical methods and differential evolution algorithms for the extraction parameters of PV module. Firstly, the analytical method is applied to simplify the equivalent circuit model and improve the efficiency of the algorithm. Then, the adaptive algorithm is used to adjust the parameters of the differential evolution algorithm. Through the algorithm proposed in this paper, the parameters of the equivalent circuit model of the photovoltaic module can be extracted by the open-circuit voltage, short-circuit current, and maximum power point current and voltage provided by the manufacturer. The proposed method is applied to the extraction of the parameters of the dual-diode equivalent circuit model of different types of photovoltaic modules. The reliability and computational efficiency of the proposed algorithm are verified by comparison and analysis.

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

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5735
Author(s):  
Mehmet Yesilbudak
Keyword(s):  
Goodness Of Fit ◽  
Search Strategy ◽  
Mean Squared Error ◽  
Photovoltaic Cells ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Current Data ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Photovoltaic Modules

With the increase in the share of solar energy in the sustainable development, accurate parameter identification plays a significant role in designing optimal solar photovoltaic systems. For this purpose, this paper extensively implements and evaluates the grey wolf optimizer with a dimension learning-based hunting search strategy, an improved version of GWO named I-GWO, in the parameter extraction of photovoltaic cells and modules. According to the experimental results, the double-diode model leads to better fitness than the other diode models in representing the physical behaviors of both photovoltaic cells and photovoltaic modules. For further performance validation, firstly, the internal parameters extracted by the I-GWO algorithm and the corresponding output current data are compared with a number of widely-used parameter extraction methods in the literature. Then, the best goodness-of-fit results achieved by the I-GWO algorithm are evaluated considering many state-of-the-art metaheuristic algorithms in the literature. The accuracy measures including root mean squared error and sum of individual absolute errors show that I-GWO is fairly promising to be the efficient and valuable parameter extraction method for both photovoltaic cells and photovoltaic modules.

scholarly journals Are streamflow recession characteristics really characteristic?

2012 ◽  
Vol 9 (9) ◽  
pp. 10563-10593 ◽  
Author(s):  
M. Stoelzle ◽  
K. Stahl ◽  
M. Weiler
Keyword(s):  
Extraction Method ◽  
Storage Capacity ◽  
Extraction Methods ◽  
Model Parameters ◽  
Catchment Scale ◽  
Model Parameterization ◽  
Recession Curves ◽  
Recession Analysis ◽  
Meso Scale

Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storage-outflow relationship. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how characteristic the resulting recession models are to distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage-outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 meso-scale catchments in Germany. We found plausible ranges for model parameterization, however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method and while they rank the catchments relatively consistent, there are still considerable differences among the methods. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of storage-outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple methods approach to investigate streamflow recession characteristics should be considered for applications whenever possible.

Research Methods for Accounting and Finance

2016 ◽  
Keyword(s):  
Research Methods ◽  
Research Process ◽  
Research Topic ◽  
First Time

An essential text for accounting and finance students undertaking research for the first time. It demystifies the research process by providing the novice researcher with a must-have guide through all of the stages of the research process, from identifying a research topic to the finished project.

scholarly journals Evaluation of DNA Extraction Methods Developed for Forensic and Ancient DNA Applications Using Bone Samples of Different Age

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 146
Author(s):  
Catarina Xavier ◽  
Mayra Eduardoff ◽  
Barbara Bertoglio ◽  
Christina Amory ◽  
Cordula Berger ◽  
...  
Keyword(s):  
Dna Extraction ◽  
Ancient Dna ◽  
Extraction Method ◽  
Fragment Size ◽  
Molecular Genetic ◽  
Extraction Methods ◽  
Degraded Dna ◽  
Size Analysis ◽  
Dna Fragments ◽  
Dna Amount

The efficient extraction of DNA from challenging samples, such as bones, is critical for the success of downstream genotyping analysis in molecular genetic disciplines. Even though the ancient DNA community has developed several protocols targeting small DNA fragments that are typically present in decomposed or old specimens, only recently forensic geneticists have started to adopt those protocols. Here, we compare an ancient DNA extraction protocol (Dabney) with a bone extraction method (Loreille) typically used in forensics. Real-time quantitative PCR and forensically representative typing methods including fragment size analysis and sequencing were used to assess protocol performance. We used four bone samples of different age in replicates to study the effects of both extraction methods. Our results confirm Loreille’s overall increased gain of DNA when enough tissue is available and Dabney’s improved efficiency for retrieving shorter DNA fragments that is beneficial when highly degraded DNA is present. The results suggest that the choice of extraction method needs to be based on available sample, degradation state, and targeted genotyping method. We modified the Dabney protocol by pooling parallel lysates prior to purification to study gain and performance in single tube typing assays and found that up to six parallel lysates lead to an almost linear gain of extracted DNA. These data are promising for further forensic investigations as the adapted Dabney protocol combines increased sensitivity for degraded DNA with necessary total DNA amount for forensic applications.

scholarly journals Impact of Extraction Method on the Detection of Quality Biomarkers in Normal vs. DFD Meat

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1097
Author(s):  
Laura González-Blanco ◽  
Yolanda Diñeiro ◽  
Andrea Díaz-Luis ◽  
Ana Coto-Montes ◽  
Mamen Oliván ◽  
...  
Keyword(s):  
Extraction Method ◽  
Protein Pattern ◽  
Stress Protein ◽  
Electrophoretic Pattern ◽  
Extraction Methods ◽  
Sds Page ◽  
Weight Protein ◽  
Triton X ◽  
Protein Extractability ◽  
Selection Of

The objective of this work was to demonstrate how the extraction method affects the reliability of biomarker detection and how this detection depends on the biomarker location within the cell compartment. Different extraction methods were used to study the sarcoplasmic and myofibrillar fractions of the Longissimus thoracis et lumborum muscle of young bulls of the Asturiana de los Valles breed in two quality grades, standard (Control) or dark, firm, and dry (DFD) meat. Protein extractability and the expression of some of the main meat quality biomarkers—oxidative status (lipoperoxidation (LPO) and catalase activity (CAT)), proteome (SDS-PAGE electrophoretic pattern), and cell stress protein (Hsp70)—were analyzed. In the sarcoplasmic fraction, buffers containing Triton X-100 showed significantly higher protein extractability, LPO, and higher intensity of high-molecular-weight protein bands, whereas the TES buffer was more sensitive to distinguishing differences in the protein pattern between the Control and DFD meat. In the myofibrillar fraction, samples extracted with the lysis buffer showed significantly higher protein extractability, whereas samples extracted with the non-denaturing buffer showed higher results for LPO, CAT, and Hsp70, and higher-intensity bands in the electrophoretic pattern. These findings highlight the need for the careful selection of the extraction method used to analyze the different biomarkers considering their cellular location to adapt the extractive process.

scholarly journals Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 240
Author(s):  
Alison Woodward ◽  
Alina Pandele ◽  
Salah Abdelrazig ◽  
Catherine A. Ortori ◽  
Iqbal Khan ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Extraction Method ◽  
Limit Of Detection ◽  
Rna Extraction ◽  
Extraction Process ◽  
Extraction Methods ◽  
Serum Starvation ◽  
Extraction Techniques ◽  
Metabolic Genes ◽  
Dual Extraction

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.

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