Conventional Space-Vector Modulation Techniques versus the Single-Phase Modulator for Multilevel Converters

Space-vector modulation is a well-suited technique to be applied to multilevel converters and is an important research focus in the last 25 years. Recently, a single-phase multilevel modulator has been introduced showing its conceptual simplicity and its very low computational cost. In this paper, some of the most conventional multilevel space-vector modulation techniques have been chosen to compare their results with those obtained with single-phase multilevel modulators. The obtained results demonstrate that the single-phase multilevel modulators applied to each phase are equivalent with the chosen wellknown multilevel space-vector modulation techniques. In this way, single-phase multilevel modulators can be applied to a converter with any number of levels and phases avoiding the use of conceptually and mathematically complex space-vector modulation strategies. Analytical calculations and experimental results are shown validating the proposed concepts

Chapter 20: What do interviewers learn? Changes in interview length and interviewer behaviors over the field period. Appendix 20

Appendix 20A Full Model Coefficients and Standard Errors Predicting Count of Questions with Individual Interviewer Behaviors, Two-level Multilevel Poisson Models with Number of Questions Asked as Exposure Variable, WLT1 and WLT2 Analytic strategyTable A20A.1 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Exact Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.2 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Nondirective Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.3 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Adequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.4 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Clarification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.5 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Appropriate Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.6 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Stuttering During Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.7 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Disfluencies with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.8 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Pleasant Talk with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.9 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Any Task-Related Feedback with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.10 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Laughter with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.11 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Minor Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.12 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Major Changes in Question Reading with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.13 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Directive Probes with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.14 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Inadequate Verification with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Table A20A.15 Coefficients and Standard Errors from Multilevel Poisson Regression Models Predicting Number of Questions with Interruptions with Total Number of Questions Asked to Each Respondent as an Exposure Variable, WLT1 and WLT2 Appendix 20B Full Model Coefficients and Standard Errors Predicting Interview Length with Sets of Interviewer Behaviors, Two-level Multilevel Linear Models, WLT1 and WLT2 Table A20B.1 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, No Interviewer Behaviors, WLT1 and WLT2 Table A20B.2 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Standardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.3 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Inefficiency Interviewer Behaviors, WLT1 and WLT2 Table A20B.4 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including Nonstandardized Interviewer Behaviors, WLT1 and WLT2 Table A20B.5 Coefficients and Standard Errors from Multilevel Linear Regression Models Predicting Total Duration, Including All Interviewer Behaviors, WLT1 and WLT2 Appendix 20C Mediation Models for Each Individual Interviewer Behavior Table A20C.1 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today 1 Table A20C.2 Indirect, Direct And Total Effect of each Interviewer Behavior on Interview Length through Interview Order, Work and Leisure Today

A Control Method for Static VAR Compensator Based On Modular Multilevel Inverter

Multilevel inverters are promised to provide a better performance in high power applications such as static VAR compensators. The proposed modular inverter has advantages compared to the conventional technologies. A control system of static VAR compensator using new modular multilevel inverter is proposed in this paper. Modeling and dynamic performance of static VAR compensator based on the proposed multilevel inverter are described in this paper. The inverter switching devices are switched at the fundamental output frequency. How to control the dc capacitor voltage is described. Several simulated results are included to verify the proposed concept. Keywords: Multilevel, inverter, STATCO

A hybrid multilevel converter for medium and high voltage applications

This paper investigates the suitability of the hybrid multilevel converter for medium and high voltage application. The converter operation, modulation, and capacitor voltage balancing method are described in detail. The ability of the hybrid multilevel converter to operate with different modulation indices and load power factors is investigated. It has been established that the hybrid multilevel converter is capable of operating independent of load power factor. Operation with variable modulation index increases voltage stresses on the converter switches and does not alter the fundamental voltage magnitude as in all known voltage source converter topologies. The viability of the hybrid multilevel converter for medium and high voltage applications is confirmed by simulations

Multilevel Quasi-Monte Carlo Methods for Lognormal Diffusion Problems

In this paper we present a rigorous cost and error analysis of a multilevel estimator based on randomly shifted Quasi-Monte Carlo (QMC) lattice rules for lognormal diffusion problems. These problems are motivated by uncertainty quantification problems in subsurface flow. We extend the convergence analysis in [Graham et al., Numer. Math. 2014] to multilevel Quasi-Monte Carlo finite element discretizations and give a constructive proof of the dimension-independent convergence of the QMC rules. More precisely, we provide suitable parameters for the construction of such rules that yield the required variance reduction for the multilevel scheme to achieve an $\varepsilon$-error with a cost of $\mathcal{O}(\varepsilon^{-\theta})$ with $\theta < 2$, and in practice even $\theta \approx 1$, for sufficiently fast decaying covariance kernels of the underlying Gaussian random field inputs. This confirms that the computational gains due to the application of multilevel sampling methods and the gains due to the application of QMC methods, both demonstrated in earlier works for the same model problem, are complementary. A series of numerical experiments confirms these gains. The results show that in practice the multilevel QMC method consistently outperforms both the multilevel MC method and the single-level variants even for non-smooth problems.Comment: 32 page

On proximity and hierarchy : exploring and modelling space using multilevel modelling and spatial econometrics

Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists‟ toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler‟s first law of geography. On the other hand, multilevel modelling using geographical units takes a more hierarchical approach. In this case the first law of geography can be rephrased as „everything is related to everything else, but things in the same region are more related than things in different regions‟. The hierarchy (multilevel) and the proximity (spatial econometrics) approach are illustrated using Belgian mobility data and productivity data of European regions. One of the advantages of a multilevel model is that it can incorporate more than two levels (spatial scales). Another advantage is that a multilevel structure can easily reflect an administrative structure with different government levels. Spatial econometrics on the other hand works with a unique set of neighbours which has the advantage that there still is a relation between neighbouring municipalities separated by a regional boundary. The concept of distance can also more easily be incorporated in a spatial econometrics setting. Both spatial econometrics and spatial multilevel modelling proved to be valuable techniques in spatial research but more attention should go to the rationale why one of the two approaches is chosen. We conclude with some comments on models which make a combination of both techniques