Abstract: The increase in electric power demand in face of
environmental issues has intensified the participation of renewable
energy sources such as photovoltaics, in the energy matrix of various
countries. Due to their operational characteristics, they can generate
time-varying harmonic and inter-harmonic distortions. For this
reason, the application of methods of measurement based on
traditional Fourier analysis, as proposed by IEC 61000-4-7, can
provide inaccurate results. Considering the aspects mentioned herein,
came the idea of the development of this work which aims to present
the results of a comparative evaluation between a methodology
arising from the combination of the Prony method with the Kalman
filter and another method based on the IEC 61000-4-30 and IEC
61000-4-7 standards. Employed in this study were synthetic signals
and data acquired through measurements in a 50kWp photovoltaic
installation.
Abstract: Low frequency power oscillations may be triggered
by many events in the system. Most oscillations are damped by the
system, but undamped oscillations can lead to system collapse.
Oscillations develop as a result of rotor acceleration/deceleration
following a change in active power transfer from a generator. Like
the operations limits, the monitoring of power system oscillating
modes is a relevant aspect of power system operation and control.
Unprevented low-frequency power swings can be cause of cascading
outages that can rapidly extend effect on wide region. On this regard,
a Wide Area Monitoring, Protection and Control Systems
(WAMPCS) help in detecting such phenomena and assess power
system dynamics security. The monitoring of power system
electromechanical oscillations is very important in the frame of
modern power system management and control. In first part, this
paper compares the different technique for identification of power
system oscillations. Second part analyzes possible identification
some power system dynamics behaviors Using Wide Area
Monitoring Systems (WAMS) based on Phasor Measurement Units
(PMUs) and wavelet technique.
Abstract: In this paper, we proposed the direct method for converting
Finite-Impulse Response (FIR) filter with low nonzero tap
into Infinite-Impulse Response (IIR) filter using the pre-determined
table. The prony method is used by ghost cancellator which is IIR
approximation to FIR filter which is better performance than IIR and
have much larger calculation difference. The direct method for many
ghost combination with low nonzero tap of NTSC(National Television
System Committee) TV signal in Korea is described. The proposed
method is illustrated with an example.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.