Noise Based Independent Component Analysis Model for Harmonic Current Estimation

2012 ◽  
Vol 433-440 ◽  
pp. 2551-2555
Author(s):  
P. Supriya ◽  
T.N. Padmanabhan Nambiar
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Analysis Model ◽  
Noise Elimination ◽  
Interconnected Power System ◽  
Harmonic Currents ◽  
Current Estimation ◽  
Simulation Results ◽  
Random Fluctuations

In a deregulating environment, Independent Component Analysis (ICA) is used to estimate the harmonic currents of non linear loads as it does not require information about the topology of the network. However, analysis is done by ignoring the effect of various noises that creep into the measurement system. In the present work, the effect of environmental noise on a simple interconnected power system with five buses is taken up. The two algorithms namely Fast ICA (FICA) and Efficient Variant Fast ICA(EFICA) are used for the analysis. A fixed noise is added and it is eliminated using whitening technique The simulation results of both algorithms show that noise elimination by whitening technique is highly successful. However, EFICA gives better results than FICA when random fluctuations of load exist rather than when fixed variations exist.

An ICA Algorithm Based on Symmetric and Asymmetric Generalized Gaussian Model

2011 ◽  
Vol 204-210 ◽  
pp. 470-475
Author(s):  
Feng Zhao ◽  
Yun Jie Zhang ◽  
Min Cai
Keyword(s):  
Independent Component Analysis ◽  
Gradient Method ◽  
Gaussian Model ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Signal Separation ◽  
Natural Gradient ◽  
Analysis Model ◽  
Practical Applications ◽  
Source Signal

Maximum likelihood estimation is a very popular method to estimate the independent component analysis model because of good performance. Independent component analysis algorithm (the natural gradient method) based on this method is widely used in the field of blind signal separation. It potentially assumes that the source signal was symmetrical distribution, in fact in practical applications, source signals may be asymmetric. This article by distinguishing that the source signal is symmetrical or asymmetrical, proposes an improved natural gradient method based on symmetric generalized Gaussian model (People usually call generalized Gaussian model) and asymmetric generalized Gaussian model. The random mixed-signal simulation results show that the improved algorithm is better than the natural gradient separation method.

Robust Method via Independent Component Analysis with Additive Noise

2010 ◽  
Vol 113-116 ◽  
pp. 272-275
Author(s):  
Yong Jian Zhao ◽  
Bo Qiang Liu ◽  
Hong Run Wang
Keyword(s):  
Independent Component Analysis ◽  
High Stability ◽  
Additive Noise ◽  
Group Structure ◽  
Processing System ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Processing System ◽  
Mixing Matrix ◽  
Simulation Results

Blind source separation via independent component analysis (ICA) has received increasing attention because of its potential application in signal processing system. The existing ICA methods can not give a consistent estimator of the mixing matrix because of additive noise. Based on interpretation and properties of the vectorial spaces of sources and mixtures, a new ICA method is presented in this paper that may constructively reject noise so as to estimate the mixing matrix consistently. This procedure may capture the underlying source dynamics effectively even if additive noise exists. The simulation results show that this method has high stability and reliability in the process of revealing the undering group structure of extracted ICA components.

scholarly journals An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm

2014 ◽  
Vol 15 (Suppl 12) ◽  
pp. S8 ◽  
Author(s):  
Lizhi Cui ◽  
Josiah Poon ◽  
Simon K Poon ◽  
Hao Chen ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Analysis Model

scholarly journals ICA-based Denoising Strategies in Breath-Hold Induced Cerebrovascular Reactivity Mapping with Multi Echo BOLD fMRI

2020 ◽  
Author(s):  
Stefano Moia ◽  
Maite Termenon ◽  
Eneko Uruñuela ◽  
Gang Chen ◽  
Rachael C. Stickland ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Intraclass Correlation ◽  
Component Analysis ◽  
Cerebrovascular Reactivity ◽  
Independent Component ◽  
Breath Hold ◽  
Analysis Model ◽  
Bold Fmri ◽  
Conventional Regression ◽  
Combined Data

AbstractPerforming a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subjectspecific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models including drifts and motion timecourses as nuisance regressors applied on singleecho or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrate the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Integrated Kalman-Independent Component Analysis Method for Harmonic Current Estimation on an Interconnected Four Bus Simulated and Laboratory Model

2012 ◽  
Vol 13 (3) ◽  
Author(s):  
P. Supriya ◽  
P. Nambiar
Keyword(s):  
Independent Component Analysis ◽  
Power System ◽  
State Estimation ◽  
Component Analysis ◽  
Independent Component ◽  
Laboratory Model ◽  
Optimal Estimator ◽  
Current Estimation ◽  
Independent Component Analysis Method ◽  
Mixing Matrix

Abstract Wide use of non-linear loads results in harmonic propagation throughout the entire power system. The harmonics generated in the power system by the harmonic injection buses need to be properly measured and quantified using minimal information about the power system network. Independent Component Analysis (ICA) provides several algorithms for harmonic state estimation, some of which are more accurate at specific harmonic frequencies. In this paper, the best ICA algorithm for steady state performance (i.e. the algorithm with the least error) is chosen and the resulting mixing matrix is processed by a Kalman Filter which functions as an optimal estimator. The harmonic state estimation is implemented on a simulated four bus system and a laboratory four bus model is also wired and the results of the work are presented.

Sign in / Sign up

Export Citation Format

Share Document