Generation of multiple bipolar voltage pulses for a phase rotation LIA
In the year 2000, a design concept of a Neutrino Factory based on Muon Storage Ring was studied at FNAL. To treat high energy spread of muons that come out of a target and decay channel and are to be accelerated before they go into the storage ring, a phase rotating scheme using a long-pulse accelerating system has been proposed. In this system the accelerating voltage is to be shaped to correct the energy spread. To implement this approach, the pulse power system has been suggested that allows forming a bipolar accelerating voltage pulse with the predefined shape. This report addresses some issues of the pulse shape optimization and describes main features of the accelerating system comprising an accelerating structure similar to that of a linear induction accelerator (LIA) and a pulser that drives it.
Fault Classification in Electric Power Transmission Lines using Support Vector Machine
Transmission lines forms the backbone of the transmission and distribution networks which powers the nation. No modern society can imagine its existence without power supplies which runs everything ranging from consumer electronics to bullet trains. Electrical power systems suffer from unexpected failures due to various random causes. Unpredicted faults that occur in power systems are required to prevent from propagation to other area in the protective system. The functions of the protective systems are to detect, then classify and finally determine the location of the faulty line of voltage and/or current line magnitudes. Then at last, for isolation of the faulty line the protective relay have to send a signal to the circuit breaker. The ability to learn, generalize and parallel processing, pattern classifiers is powerful applications of machine learning used as an intelligent means for detection. This research paper focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection and fault classification have been achieved by using support vector machines. The SVM based classifier is trained on the fault database to classify transient single phase to ground faults. Simulation results have been provided to demonstrate that Support Vector Machine based methods are efficient in locating faults on transmission lines and achieve satisfactory performances. Simulation of three phase transmission line is done in MATLAB and results are compared in context of the fault classification and fault location.
Monte Carlo simulations of magnetovolume instabilities in anti-Invar systems
We perform constant pressure Monte Carlo simulations of a spin-analogous model which describes coupled spatial and magnetic degrees of freedom on an fcc lattice. Our calculations qualitatively reproduce magnetovolume effects observed in some rare earth manganese compounds, especially in the anti-Invar material YMn2. These are a sudden collapse of the magnetic moment which is connected with a huge volume change, and a largely enhanced thermal expansion coefficient.