16 results on '"derived model"'
Search Results
2. Modelling of DFIG-based wind turbine for low-frequency oscillation analysis of power system with high penetration of distributed energy
- Author
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Yunhui Huang, Weihao Chen, Xiangtian Deng, Jinrui Tang, Guorong Zhu, and Haitao Zhang
- Subjects
oscillations ,wind power plants ,asynchronous generators ,power system stability ,modal analysis ,wind turbines ,derived model ,Thévenin equivalent model ,signal stability analysis ,reduced order model ,doubly-fed induction generator-based wind turbines ,distributed energy ,high penetration ,power system ,low-frequency oscillation analysis ,DFIG-based wind turbine ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
This study investigates modelling of doubly-fed induction generator (DFIG)-based wind turbines for low-frequency oscillation analysis of power systems with high penetration of distributed energy. A reduced order model of wind turbine with DFIG is proposed for small signal stability analysis in electromechanical time scale for low-frequency oscillation analysis. Furthermore, a Thévenin equivalent model of DFIG with internal voltage is also presented. Base on the derived model, the effect of phase-locked loop is emphasised, and explained by using the internal voltage derived. The model proposed is well verified by modal analysis and time-domain simulations.
- Published
- 2019
- Full Text
- View/download PDF
3. First determination on two kinds of microplastic-air partition coefficients of seven per- and polyfluoroalkyl substances under environmentally relative conditions: Experiment measurement and model prediction.
- Author
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Deng, Yun, Peng, Lin, Li, Zhendong, Xu, Wang, Ren, Gang, and Wang, Fei
- Subjects
- *
FLUOROALKYL compounds , *STANDARD deviations , *HIGH density polyethylene , *PREDICTION models , *RESPONSE surfaces (Statistics) - Abstract
Microplastics (MPs) in the environment are the sink and vector of organic contaminants, including per- and polyfluoroalkyl substances (PFASs). Although PFASs are low- and non-volatile compounds, they have the potential to partition and diffuse from MP into the gas phase in the environmental functions. Herein, the MP-air partition coefficient (K PA) of seven PFASs was measured using a solid-fugacity meter. The PFAS K PA values in two MPs (high-density polyethylene (HDPE) and thermoplastic polyurethane (TPU)) were determined under different times, temperatures, and relative humidities (RH), and a model was developed to predict the PFAS K PA values based on the measured data. The results showed that the K PA of PFASs increased with the prolonged partition time until 90 mins, and higher temperature and RH facilitated the distribution of PFASs in MPs into the air phase, leading to smaller K PA values. Moreover, the derived equation for predicting PFAS log K PA values was robust with 0.79 of an adjusted square of correlation coefficient (R2 adjusted = 0.79) and 0.35 of root mean squared error (RMSE = 0.35). These findings provided the first knowledge for understanding the partition behavior and fate of PFASs in the MP-air microenvironment. [Display omitted] • The K PA of seven PFASs was determined at environmentally relative conditions through a solid-fugacity meter. • The partition of PFASs between MPs and gas was equilibrium when partition time reached 90 mins. • Increasing temperature and RH resulted in the reduction of PFAS K PA. • A derived model based on the measured data could effectively predict the K PA of PFAS. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Ten Things You Should Know about the Dynamic Conditional Correlation Representation
- Author
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Massimiliano Caporin and Michael McAleer
- Subjects
DCC representation ,BEKK ,GARCC ,stated representation ,derived model ,conditional correlations ,two step estimators ,assumed asymptotic properties ,filter ,Economics as a science ,HB71-74 - Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
- Full Text
- View/download PDF
5. The envelope of a pointclass under a local determinacy hypothesis.
- Author
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Wilson, Trevor M.
- Subjects
- *
ENVELOPES (Stationery) , *STATISTICAL hypothesis testing , *LIMITS (Mathematics) , *SET theory , *ARGUMENT - Abstract
Given an inductive-like pointclass Γ ˜ and assuming the Axiom of Determinacy, Martin identified and analyzed a pointclass that contains the prewellorderings of the next scale beyond Γ ˜ if such a scale exists. We show that much of Martin's analysis can be carried out assuming only ZF + DC R and Δ ˜ Γ ˜ determinacy by adapting arguments of Kechris and Woodin [10] and Martin [13] . This generalization can be used to show that every set of reals is Suslin in the intersection of two divergent models of A D + , giving a new proof of a theorem of Woodin, as well as to show that every set of reals is Suslin in the derived model at an indestructibly weakly compact limit of Woodin cardinals. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
6. Med-long term system structure forecasting of power consumption based on grey derived model.
- Author
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Yichun, Wu, Zhenying, Cheng, and Miao, Li
- Abstract
Med-long term load forecasting is the basis of power system planning. According to the characteristics and changing rules of the different types of electricity load and different demand side management strategies on them, electricity load structure forecasting for the research on power development and planning is very necessary. Based on the grey theory, this paper proposes a med-long term load structure forecasting model in which the system state equations and grey dynamic model group about various types of electricity load are established, in terms of the system dominant factors and associated factors determined by the grey correlative degree analysis method, and are solved to realize the med-long term structure forecasting of power consumption by means of the GM (1, N, x (0)) model derived from GM (1, N) model. The power consumption of actual grid is predicted in medium and long term in a case study utilizing the proposed model. The prediction results are analyzed and compared with the observed values of power consumption, which verifies the validity and practicality of the established med-long term load forecasting model. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
- View/download PDF
7. STRUCTURE THEORY OF L(ℝ, μ) AND ITS APPLICATIONS.
- Author
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TRANG, NAM
- Subjects
CARDINAL numbers ,AXIOMATIC set theory ,SET theory ,MATHEMATICS theorems ,EQUIVALENCE classes (Set theory) - Abstract
In this paper, we explore the structure theory of L(ℝ, μ) under the hypothesis L(ℝ, μ) ⊧ “AD + μ is a normal fine measure on ” and give some applications. First we show that “ ZFC + there exist ω2 Woodin cardinals”1 has the same consistency strength as “ AD + ω1 is ℝ-supercompact”. During this process we show that if L(ℝ, μ) ⊧ AD then in fact L(ℝ, μ) ⊧ AD+. Next we prove important properties of L(ℝ, μ) including Σ1 -reflection and the uniqueness of μ in L(ℝ, μ). Then we give the computation of full HOD in L(ℝ, μ). Finally, we use Σ1 -reflection and ℙmax forcing to construct a certain ideal on (or equivalently on in this situation) that has the same consistency strength as “ZFC+ there exist ω2 Woodin cardinals.” [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
8. Models derived from GM(1,1) power model.
- Author
-
WANG Zheng-xin
- Subjects
- *
SYSTEMS theory , *MATHEMATICAL optimization , *QUADRATIC forms , *LINEAR control systems , *MATHEMATICAL models - Abstract
In order to further complement the system of grey power models, this paper analyzes the transformation relationships between the classic GM(1,1) model and GM(1,1) power model, and deduces five derived models including GM(1,1,x(2)) power model, GM(1,1,x(1)) power model, GM(1,1,6) power model, GM(1,1,exp) power model and GM(1,1,C) power model based on the defined and whitened GM(1,1) power models. A group of grey power models including classic GM(1,1) power model and the derived five models is built finally. The results show that time response functions of GM(1,1) model and GM(1,1) power model are essentially the same. Different derived models have different structures, contents and solutions, reflecting the principal of non-unique solution in grey system theory. In practical applications, the most appropriate whitened solution can be identified from group of default solutions under the corresponding objectives. [ABSTRACT FROM AUTHOR]
- Published
- 2013
9. On the Models Derived from Resource Efficacy Model.
- Author
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Wang Zheng-xin and Deng Julong
- Subjects
- *
GREY relational analysis , *MATHEMATICAL models , *CURVES , *LATTICE theory , *SYSTEMS theory , *UNIQUENESS (Mathematics) - Abstract
Grey resource efficacy model is effective for describing the developing stage (rapid varying phase) of resource efficacy S curve. In order to further complement the model system, this paper deduces five derived models based on the original resource efficacy model proposed by Professor Deng. The properties of these derived models are then studied respectively. The results show that different models have different structures, contents, solutions and properties, implying the principal of non-uniqueness in grey system theory. [ABSTRACT FROM AUTHOR]
- Published
- 2012
10. Synthesis and structural, crystallographic, electronic, chemical and optical characterizations of alpha-diisopropylammonium bromide (α-DIPAB) thin films.
- Author
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Alsaad, A.M., Al-Bataineh, Qais M., Bani-Salameh, Areen A., Ahmad, A.A., Albiss, B.A., Telfah, Ahmad, and Sabirianov, R.F.
- Subjects
- *
THIN films , *VAN der Waals forces , *VIBRATIONAL spectra , *SURFACE cracks - Abstract
Thin films of the promising Diisopropylammonium Bromide (α-DIPAB) are synthesized by the slow evaporation technique. We report on the synthesis and characterization of as-grown (α-DIPAB) thin films. In particular, structural, crystallographic, electronic, chemical, and optical properties are measured and interpreted. To elucidate structural and crystallographic properties, X-ray diffraction (XRD) measurements are conducted to reveal that α-DIPAB thin film is polycrystalline and exhibits a monoclinic structure. Furthermore, Density Functional Theory (DFT)-based simulations are performed to calculate the structural and electronic parameters of the α-DIPAB thin film. An excellent agreement between the measured and calculated structural parameters is obtained. Taking into account the Van der Waals forces using the HSE06 method yields an optical band gap (≈6 eV) in good agreement with the experimental value. Scanning Electronic Microscopy (SEM) micrographs demonstrate that thin-film surface cracks appear dividing the surface into ice land-like regions. The coarse features in the short-scaled micrograph are characterized by the micro-sheet-like with cracked boundaries. Moreover, the role of the interactions between molecules mediated by bromine ions is revealed. The vibrational spectra correspond to a well-ordered crystal. The mechanism behind the large spontaneous polarization of thin films is explained. The interplay between electronic and optical properties of thin films is investigated using UV-Vis measurements and the recently published mathematical derived model. An excellent agreement between measured and calculated electronic and optical parameters indicates the validity and feasibility of the model. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
11. Ten Things You Should Know About the Dynamic Conditional Correlation Representation
- Author
-
Michael McAleer and Massimiliano Caporin
- Subjects
Diagonal ,Nuclear Theory ,DCC representation, BEKK, GARCC, stated representation, derived model, conditional covariances, conditional correlations, regularity conditions, moments, two step estimators, assumed properties, asymptotic properties, filter, diagnostic check ,Correlation ,conditional correlations ,Econometrics ,C58 ,GARCC ,Special case ,Nuclear Experiment ,Mathematics ,G17 ,filter ,C18 ,derived model ,Estimator ,lcsh:Economics as a science ,GARCH models ,jel:C58 ,DCC representation ,BEKK ,stated representation ,two step estimators ,assumed asymptotic properties ,jel:G17 ,jel:C18 ,Computer Science::Programming Languages ,Theorie ,Economics and Econometrics ,jel:C01 ,jel:B23 ,jel:C ,jel:C00 ,Studentized residual ,ddc:330 ,jel:C1 ,jel:C2 ,jel:C3 ,jel:C4 ,Representation (mathematics) ,jel:C5 ,C32 ,Statistische Methode ,jel:C8 ,lcsh:HB71-74 ,Filter (signal processing) ,jel:C32 ,Financial econometrics ,Korrelation - Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
12. Ten Things You Should Know About the Dynamic Conditional Correlation Representation
- Author
-
Caporin, Massimiliano and McAleer, Michael
- Subjects
Nuclear Theory ,conditional correlations ,asymptotic properties ,ddc:330 ,DCC representation ,C58 ,two step estimators ,GARCC ,Nuclear Experiment ,C32 ,BEKK ,Statistische Methode ,G17 ,filter ,assumed properties ,C18 ,derived model ,diagnostic check ,conditional covariances ,stated representation ,moments ,Computer Science::Programming Languages ,Econometría ,Korrelation ,Theorie ,regularity conditions - Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
13. Ten Things You Should Know About DCC
- Author
-
Massimiliano Caporin and Michael McAleer
- Subjects
DCC, BEKK, GARCC, Stated representation, Derived model, Conditional covariances, Conditional correlations, Regularity conditions, Moments, Two step estimators, Assumed properties, Asymptotic properties, Filter, Diagnostic check ,jel:G17 ,DCC ,BEKK ,GARCC ,Stated representation ,Derived model ,Conditional covariances ,Conditional correlations ,Regularity conditions ,Moments ,Two step estimators ,Assumed properties ,Asymptotic properties ,Filter ,Diagnostic check ,jel:C18 ,Nuclear Theory ,Computer Science::Programming Languages ,jel:C32 ,Nuclear Experiment ,jel:C58 - Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time- varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
14. Ten Things you should know about DCC
- Author
-
Caporin, Massimiliano and McAleer, Michael
- Subjects
Moments ,Nuclear Theory ,Diagnostic check ,Assumed properties ,Asymptotic properties ,ddc:330 ,C58 ,GARCC ,Nuclear Experiment ,C32 ,BEKK ,Statistische Methode ,DCC ,G17 ,Conditional correlations ,Derived model ,Filter ,C18 ,Two step estimators ,Conditional covariances ,Computer Science::Programming Languages ,Stated representation ,Regularity conditions ,Econometría ,Korrelation ,Theorie - Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
15. Ten Things you should know about DCC
- Author
-
Caporin, M. (Massimiliano), McAleer, M.J. (Michael), Caporin, M. (Massimiliano), and McAleer, M.J. (Michael)
- Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
16. Ten Things You Should Know About the Dynamic Conditional Correlation Representation
- Author
-
Caporin, M. (Massimiliano), McAleer, M.J. (Michael), Caporin, M. (Massimiliano), and McAleer, M.J. (Michael)
- Abstract
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
- Published
- 2013
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