Estimation and Elimination of Power System Harmonics and Implementation of Kalman Filter Algorithm

With the extensive implementation of power circuit devices, mainly rectifier, inverters, switches in power system and manufacturing industries results in serious problem relating to the quality of power. One of the major issues is a production of harmonics for current and voltage causing alteration in output waveform, voltage-distortion, voltage degrading, equipment local heating, etc. Loads which are non-linear such as UPS, SMPS, and speed drives results in production of harmonics in current waveform.These draws in the component of current having reactive power from the bus bar, and thus, causes an imbalance in bus current waveform. Hence to eliminate the problems of harmonics we need to compensate the component of harmonic causing such trouble. With all the existing methods used, one of the method being minimizing harmonic in power utility via SAPF. Hence this Paper suggests a complete analysis of SAPF performance by applying two current control strategies. First being instantaneous active and reactive power theory (p-q) and second being synchronous frame reference theory (d-q) and analyzing their overall performance to select one of the above methods. Harmonic current controller is described and used which provides correct gating signals for the IGBT based inverter nad thus helps in eliminating harmonic components. Also, this Paper explains the Kalman Filter implementation in real life scenario in frequency calculation taking an suitable example

Study and Analysis of different types of comparator

Different types of comparators are studied and the circuits are simulated in Cadence® Virtuoso Analog Design Environment using GPDK 90nm technology. The circuits are simulated with 1.8 Volt DC supply voltage. The clock has a frequency of 250 MHz. All the respective DC responses and transient responses are plotted and analyzed. Layouts of all the comparators have been done in Cadence® Virtuoso Layout XL Design Environment. Different static and dynamic characteristics of all the comparators are studied and compared. Their advantages and disadvantages were also discussed

Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampled across time and space. A novel machine learning method is proposed for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). Proposed hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non-parametric regression with GP is performed. The methodology is applied for modeling groundwater levels across the CV during 2015 - 2020. We demonstrate the efficacy of GP-DNN regression for modeling non-stationary features in the well data with fast and reliable uncertainty quantification. Our results indicate that the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.Comment: Submitted to Water Resources Researc

Ratio of neutrophilic CD64 and monocytic HLA-DR: a novel parameter in diagnosis and prognostication of neonatal sepsis

Objective: Approaches to monitoring of sepsis have traditionally relied upon the pro-inflammatory component of the sepsis response. This study evaluated the diagnostic and prognostic potential of the ratio of neutrophilic CD64 (nCD64) and monocytic HLA-DR (mHLA-DR) median fluorescence index in monitoring of neonatal sepsis. Methods: Blood from 100 neonates suspected of sepsis and 29 healthy controls was collected on clinical suspicion of sepsis, and the expression of nCD64, mHLA-DR was evaluated by Flow Cytometry; thereby, a derived parameter “Sepsis index,” SI = nCD64/mHLA-DR × 100 was estimated. Results: At day 1, sensitivity and specificity to detect sepsis using nCD64 was 73.01% and 89.18%, respectively, while for SI it was 73.01% and 72.22%, respectively. On Kaplan-Meier analysis, neonates with SI > cut-off showed a higher 30 day-mortality than those with low SI (P = 0.096). On multivariate analysis, the factor associated with mortality in our cohort was Apgar score ≤3, while SI showed a trend toward significance. Conclusions: At day1, nCD64 is useful for the diagnosis of neonatal sepsis whereas mHLA-DR is beneficial for monitoring patients at a later time point. The SI is a marker of moderate diagnostic sensitivity and supplements the current arsenal of laboratory investigations to detect neonatal sepsis. As a marker of prognosis, a high SI shows a trend towards greater mortality

Effect of Manganese (II) Oxide on microstructure and ionic transport properties of nanostructured cubic zirconia

Effect of MnO addition on microstructure and ionic transport properties of nanocrystalline cubic(c)-ZrO2 is reported. Monoclinic (m) ZrO2 powders with 10-30 mol% MnO powder are mechanically alloyed in a planetary ball mill at room temperature for 10 h and annealed at 550 degrees C for 6 h. In all compositions m-ZrO2 transforms completely to nanocrystalline c-ZrO2 phase and MnO is fully incorporated into c-ZrO2 lattice. Rietveld's refinement technique is employed for detailed microstructure analysis by analyzing XRD patterns. High resolution transmission electron microscopy (HRTEM) analysis confirms the complete formation of c-ZrO2 phase. Presence of stoichiometric Mn in c-ZrO2 powder is confirmed by Electron Probe Microscopy analysis. XPS analysis reveals that Mn is mostly in Mn2+ oxidation state. A correlation between lattice parameter and oxygen vacancy is established. A detailed ionic conductivity measurement in the 250 degrees-575 degrees C temperature range describes the effect of MnO on conductivity of c-ZrO2. The ionic conductivity (s) of 30 mol% MnO alloyed ZrO2 at 550 degrees C is 0.04 s cm(-1). Electrical relaxation studies are carried out by impedance and modulus spectroscopy. Relaxation frequency is found to increase with temperature and MnO mol fraction. Electrical characterization predicts that these compounds have potentials for use as solid oxide fuel cell electrolyte material. (C) 2015 Elsevier Ltd. All rights reserved

Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction

COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in ‘flattening the curve’ and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. This kind of quantum computational approach can be useful in reducing space and time complexities of a huge amount of information related to the epidemic