Three phase boost rectifier design

An electric power can be converted from one form to another form by using power electronics devices. The function of power electronics circuits by using semiconductor devices as switch is modifying or controlling a voltage. The goal of power electronics circuits are to convert electrical energy from one form to another, from source to load with highest efficiency, high availability and high reliability with the lowest cost, smallest size and weight. The term rectification refers to the power circuit whose function is to alter the ac characteristic of the line electric power to produce a “rectified”ac power at the load side that contain the dc value In this project, a study has done for the two types of rectifier topology of alternating current to direct current voltage of a three-phase boost rectifier with pulse width modulation (PWM) and a threephase boost rectifier with active power filter (APF). Power factor, shape distortion and voltage can be increased as much as seen through two types of this topology if it is connected to the non-linear loads in power systems. Three phase rectifier with pulsewidth modulation (PWM) is one of controlled rectifier consist six pulses divides into two groups which are top group and bottom group. For top group, IGBT with its collector at the highest potential will conduct at one time. The other two will be reversed. Thus for bottom group, IGBT with the its emitter at the lowest potential will conduct. This project also observes the current, voltage waveform and the harmonics component when the active power filter (AFC) placed in series with non-linear load. Type of rectifier used is uncontrolled rectifier. In this work MATLAB/SIMULINK power system toolbox is used to simulate the system Results of simulations carried out, the advantages and disadvantages, the increase in voltage and waveform distortion for the system under consideration can be show

Reflective jacket

Safety product is created for workers, students, people and society to prevent from dangerous, harmful, injured also risks situation that can be occurs before, during and after works. The materials to produce the safety product must be strong, hard, good resistance, fast treatment and others characteristic that can be protect and prevent the user from the dangerous. Sometimes, the cost to produce the safety product is expensive because of the material to make the safety product become the good products

Efficient and Highly Robust Hotelling T² Control Charts Using Reweighted Mininum Vector Variance

Hotelling T² control chart is an effective tool in statistical process control for multivariate environment. However, the performance of traditional Hotelling T² control chart using classical location and scatter estimators is usually marred by the masking and swamping effects. In order to alleviate the problem, robust estimators are recommended. The most popular and widely used robust estimator in the Hotelling T² control chart is the minimum covariance determinant (MCD). Recently, a new robust estimator known as minimum vector variance (MVV) was introduced. This estimator possesses high breakdown point, affine equivariance and is superior in terms of computational efficiency. Due to these nice properties, this study proposed to replace the classical estimators with the MVV location and scatter estimators in the construction of Hotelling T² control chart for individual observations in Phase II analysis. Nevertheless, some drawbacks such as inconsistency under normal distribution, biased for small sample size and low efficiency under high breakdown point were discovered. To improve the MVV estimators in terms of consistency and unbiasedness, the MVV scatter estimator was multiplied by consistency and correction factors respectively. To maintain the high breakdown point while having high statistical efficiency, a reweighted version of MVV estimator (RMVV) was proposed. Subsequently, the RMVV estimators were applied in the construction of Hotelling T² control chart. The new robust Hotelling T² chart produced positive impact in detecting outliers while simultaneously controlling false alarm rates. Apart from analysis of simulated data, analysis of real data also found that the new robust Hotelling T² chart was able to detect out of control observations better than the other charts investigated in this study. Based on the good performance on both simulated and real data analysis, the new robust Hotelling T² chart is a good alternative to the existing Hotelling T² charts

Statistical Process Control Using Modified Robust Hotelling's T² Control Charts

Hotelling’s T² chart is a popular tool for monitoring statistical process control. However, this chart is sensitive to outliers. To alleviate the problem, three approaches to the robust Hotelling’s T² chart namely trimming, Winsorizing and median based were proposed. These approaches used robust location and scale estimators to substitute for the usual mean and covariance matrix, respectively. For each approach, three robust scale estimators: MADn, Sn and Tn were introduced, and these estimators functioned accordingly to the approach. The first approach, denoted as T²t, applied the concept of trimming via Mahalanobis distance. The robust scale estimator was used to replace the covariance matrix in Mahalanobis distance. The trimmed mean and trimmed covariance matrix were the location and scale estimators for the T²t chart. The second approach,, T²w, employed each scale estimator as the Winsorized criterion. This approach applied Winsorized modified one step M-estimator and its corresponding Winsorized covariance as the location and the scale matrix for T²w chart, respectively. Meanwhile, in the third approach, T²н, the robust scale estimator took the role of the scale matrix with Hodges-Lehman as the location estimator. This approach worked with the original data without any trimming or Winsorizing. Altogether, nine robust control charts were proposed. The performance of each robust control chart was assessed based on false alarm rates and probability of detection. To investigate on the strengths and weaknesses of the proposed charts, various conditions were created by manipulating four variables, namely number of quality characteristics, proportion of outliers, degree of mean shifts, and nature of quality characteristics (independent and dependent). In general, the proposed charts performed well in terms of false alarm rates. With respect to probability of detection, all the proposed charts outperformed the traditional Hotelling's T² charts. The overall findings showed that, the proposed robust Hotelling's T² control charts are viable alternatives to the disputed traditional charts

A Comparison of Some Robust Bicariate Control Charts for Individual Observations

This paper proposed and considered some bivariate control charts to monitor individual observations from a statistical process control. Usual control charts which use mean and variance-covariance estimators are sensitive to outliers. We consider the following robust alternatives to the classical Hoteling’s T2: T2MedMAD, T2MCD, T2MVE A simulation study has been conducted to compare the performance of these control charts. Two real life data are analyzed to illustrate the application of these robust alternatives

On Data Depth and the Application of Nonparametric Multivariate Statistical Process Control Charts

The purpose of this article is to summarize recent research results for constructing nonparametric multivariate control charts with main focus on data depth based control charts. Data depth provides data reduction to large-variable problems in a completely nonparametric way. Several depth measures including Tukey depth are shown to be particularly effective for purposes of statistical process control in case that the data deviates normality assumption. For detecting slow or moderate shifts in the process target mean, the multivariate version of the EWMA is generally robust to non-normal data, so that nonparametric alternatives may be less often required

Bivariate modified hotelling’s T2 charts using bootstrap data

The conventional Hotelling’s  charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s  charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively.  The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested

Robust linear discriminant analysis using MOM-Qn and WMOM-Qn estimators: Coordinate-wise approach

Robust linear discriminant analysis (RLDA) methods are becoming the better choice for classification problems as compared to the classical linear discriminant analysis (LDA) due to their ability in circumventing outliers issue. Classical LDA relies on the usual location and scale estimators which are the sample mean and covariance matrix. The sensitivity of these estimators towards outliers will jeopardize the classification process. To alleviate the issue, robust estimators of location and covariance are proposed. Thus, in this study, two RLDA for two groups classification were modified using two highly robust location estimators namely Modified One-Step M-estimator (MOM) and Winsorized Modified One-Step M-estimator (WMOM). Integrated with a highly robust scale estimator, Qn, in the trimming criteria of MOM and WMOM, two new RLDA were developed known as RLDAMQ and RLDAWMQ respectively. In the computation of the new RLDA, the usual mean is replaced by MOM-Qn and WMOM-Qn accordingly. The performance of the new RLDA were tested on simulated as well as real data and then compared against the classical LDA. For simulated data, several variables were manipulated to create various conditions that always occur in real life. The variables were homogeneity of covariance (equal and unequal), samples (balanced and unbalanced), dimension of variables, and the percentage of contamination. In general, the results show that the performance of the new RLDA are more favorable than the classical LDA in terms of average misclassification error for contaminated data, although the new RLDA have the shortcoming of requiring more computational time. RLDAMQ works best under balanced sample sizes while RLDAWMQ surpasses the others under unbalanced sample sizes. When real financial data were considered, RLDAMQ shows capability in handling outliers with lowest misclassification error. As a conclusion, this research has achieved its primary objective which is to develop new RLDA for two groups classification of multivariate data in the presence of outliers

Implementation and Application of Statistical Methods in Research, Manufacturing Technology and Quality Control

Práce se zabývá možnostmi použití moderních statistických postupů se zaměřením na robustní metody. Vybrané postupy jsou analyzovány a aplikovány na častých problémech z praxe v českém průmyslu a technologii. Studovaná témata, metody a algoritmy jsou voleny tak, aby byla přínosem v reálných aplikacích ve srovnání s používanými klasickými metodami. Použitelnost a účinnost algoritmů je ověřena a demonstrována na reálných studiích a problémech z výzkumného prostředí českých průmyslových subjektů. V práci je poukázáno na nevyužitý potenciál současné teoreticko-matematické a výpočetní kapacity a nových přístupů k chápání statistických modelů a metod. Výsledkem práce je rovněž původní vývojové prostředí s programovacím jazykem DARWin (Data Analysis Robot for Windows) pro intenzivní využití efektivních numerických postupů pro získávání informací z dat. Práce je impulsem pro širší využití robustních a numericky, nebo výpočetně náročnějších metod, jako jsou neuronové sítě, pro modelování procesů a kontrolu kvality.This thesis deals with modern statistical approaches and their application aimed at robust methods and neural network modelling. Selected methods are analyzed and applied on frequent practical problems in czech industry and technology. Topics and methods are to be benificial in real applications compared to currently used classical methods. Applicability and effectivity of the algorithms is verified and demonstrated on real studies and problems in czech industrial and research bodies. The great and unexploited potential of modern theoretical and computational capacity and the potential of new approaces to statistical modelling and methods. A significant result of this thesis is also an environment for software application development for data analysis with own programming language DARWin (Data Analysis Robot for Windows) for implemenation of effective numerical algorithms for extaction information from data. The thesis should be an incentive for boarder use of robust and computationally intensive methods as neural networks for modelling processes, quality control and generally better understanding of nature.