Development of power factor correction using boost converter circuit

This project describes on developed of a system that can control current by using Texas Instruments microcontroller with Code Compressor Studio software. The type of controller used is C2000 Microcontroller (Texas Instrument TMS320F28) for current feedback loop application. The connection between PC as the software for Code Compressor Studio software (CCS), Pulse Width Modulation (PWM), Texas Instrument (interface), gate driver, power factor correction circuit in boost circuit topology and rectifier circuit, load and sensor are the main parts in this project. Texas Instrument works as the interface communication with MATLAB SIMULINK 2015a to the power factor correction system. In order to control the triggering of the MOSFETs in the Power Factor Correction (PFC), the Pulse Width Modulation (PWM) is needed from MATLAB SIMULINK that has been applied in this project. The main objective in this project is to control the current output by using microcontroller and to detect the load current in order to have the closed loop feedback. By designing the rectifier and the boost circuit in MATLAB simulation is the first part of the project, which gives Total Harmonic Distortion in opened loop 23.86 % and meanwhile for about 17.66 % in closed based. At the same time the hardware part has also been assemble. The current controller loop has been designed according to mathematical equations which is achieved the results of efficiency and stability in output voltage and the input current of the source. With 22 V AC voltage input the obtained output of the rectifier circuit voltage is at 21.2 V DC voltage with 0.139 mA current. Meanwhile the boost circuit boosted the output voltage to 31.6 V DC voltage with 1.05 mA amount of current. The project has been accomplished and achieved all the objectives successfully

Colimits in the correspondence bicategory

We interpret several constructions with C*-algebras as colimits in the bicategory of correspondences. This includes crossed products for actions of groups and crossed modules, Cuntz-Pimsner algebras of proper product systems, direct sums and inductive limits, and certain amalgamated free products.Comment: Final versio

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.Peer reviewe

Blind Two-Dimensional Super-Resolution and Its Performance Guarantee

In this work, we study the problem of identifying the parameters of a linear system from its response to multiple unknown input waveforms. We assume that the system response, which is the only given information, is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the linear system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain randomness and concentration properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications such as radar imaging, image restoration, and indoor source localization. In this framework, we show that under a minimum separation condition between the time-frequency shifts, all the unknowns that characterize the linear system can be recovered precisely and with very high probability provided that a lower bound on the total number of the observed samples is satisfied. The proposed framework is based on 2D atomic norm minimization problem which is shown to be reformulated and solved efficiently via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided

Determinants of Privatisation in Selected Sub-Saharan African Countries: Is Privatisation Politically Induced?

While African governments and international donors generally support privatisation for stabilisation as an imperative of public finance problems, academicians are more inclined to discuss the efficiency gains of privatisation. Antithetically, some political economists argue that whereas African governments actually de-emphasise privatisation, donors mainly insist on privatisation to promote neoclassical views without offering an alternative to state reform. This paper realises that the main purposes of privatisation in Sub-Saharan Africa have, so far, been multidimensional. It envisages, empirically, the determinants of privatisation in Sub-Saharan Africa using a probit model over the period 1970-1994. The results are supportive of the hypothesis that privatisation in the sample countries is induced by macro-instability and political bias.

Model-based fault detection and isolation for wind turbine

In this paper, a quantitative model based method is proposed for early fault detection and diagnosis of wind turbines. The method is based on designing an observer using a model of the system. The observer innovation signal is monitored to detect faults. For application to the wind turbines, a first principles nonlinear model with pitch angle and torque controllers is developed for simulation and then a simplified state space version of the model is derived for design. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. A multiobjective optimization method is then employed to solve this dual problem. Simulation results are presented to demonstrate the performance of the proposed method

Wind turbine control using PI pitch angle controller

This paper suggests two methods to calculate the gains of a proportional-Integral pitch angle controller for a 5 MW wind turbine. The first method is analytical and the second one is based on simulation. Firstly, the power coefficient characteristics for different pitch angles are calculated. Secondly, the output powers vs. rotor speed curves from cut-in to cut-out wind speeds are simulated. The results from first and second analyses used to find the control gains at different wind speeds. Finally, the results are compared using a wind turbine model to determinate turbine’s tracking characteristic

Influence of alleycropping microclimate on the performance of groundnut (Arachis hypogaea L.) and sesame (Sesamum indicum L.) in the semi-desert region of northern Sudan

An alley cropping system was established at Hudieba Research Station (17.57’N and 33.8’ E) on a loamy sand soil of the semi-desert region of northern Sudan. The objective of this study was to investigate the influence of modified microclimate in 6-m wide alleys formed by Acacia ampliceps and Acacia stenophylla on growth and yield of groundnut. and sesame. Above-ground interactions were determined by measuring air temperature, relative humidity, wind speed, solar energy and shade length and behaviour. Groundnut and sesame were evaluated for growth and yield by laying out sample plots at southern, central and northern part of the alleys and at control plots. Due to microclimatic modifications in the alleys, the yield of both crops in the alleys significantly (p=0.01) exceeded that of the sole crop. Yield reduction at the northern alley was fully compensated by high yield increase at southern and central alleys. The yield of groundnut increased by 37.7 and 19.6 % in the A.stenophylla and A.ampliceps alleys, respectively. On the other hand, the yield of sesame increased with the stenophylla-alley (+40.3%), while it decreased with ampliceps-alley (-51.5%). The results indicated that the competition for light was the major factor contributing to the increase or reduction of growth and yield of groundnut and sesame