Analysis of Three Phase Signal using Wigner Spectrum

New method of observation and diagnosis of inverter-fed induction motor drives is developed and tested. Unsymmetrical conditions concerning the machine impedances or valves operation are reflected in the spectrum of the current spacephasor. We estimate the spectrum of the space-phasor with the help of the Wigner- Ville distribution (WVD) and we obtain its time-frequency representation with excellent time and frequency resolution. The proposed method is tested with nonstationary multiple-component signals occurring during the fault operation of inverter-fed drives and transmission lines

Detection of Remote Harmonics Using SVD

The paper examines the singular value decomposition (SVD) for detection of remote harmonics in signals, in the presence of high noise contaminating the measured waveform. When the number of harmonics is very large and at the same time certain harmonics are distant from the other, the conventional frequency detecting methods are not satisfactory. The methods developed for locating the frequencies as closely spaced sinusoidal signals are appropriate tools for the investigation of power system signals containing harmonics differing significantly in their multiplicity. The SVD methods are ideal tools for such cases. To investigate the methods several experiments have been performed. For comparison, similar experiments have been repeated using the FFT with the same number of samples and sampling period. The comparison has proved an absolute superiority of the SVD for signals burried in noise. However, the SVD computation is much more complex than the FFT, and requires more extensive mathematical manipulations

Interplay of disorder and interaction in Majorana quantum wires

We study the interplay between disorder and interaction in one-dimensional topological superconductors which carry localized Majorana zero-energy states. Using Abelian bosonization and the perturbative renormalization group (RG) approach, we obtain the RG-flow and the associated scaling dimensions of the parameters and identify the critical points of the low-energy theory. We predict a quantum phase transition from a topological superconducting phase to a non-topological localized phase, and obtain the phase boundary between these two phases as a function of the electron-electron interaction and the disorder strength in the nanowire. Based on an instanton analysis which incorporates the effect of disorder, we also identify a large regime of stability of the Majorana-carrying topological phase in the parameter space of the model.Comment: New version includes a section and an appendix with a detailed study on the effect of interaction and disorder on the stability of Majorana end-states. 6 pages, 1 figur

Analysis of power quality disturbances using the S-Transform

The method presented in this paper employs recently introduced S-transform which is an important development of STFT with improved properties. Proposed methods allow tracking changes in amplitude and frequency with better precision than STFT and Wigner-Ville transform. Possible applications in diagnosis and power quality problems are targeted. The S-transform outperforms the STFT in that it has a better resolution in phase space giving a fundamentally more sound time frequency representation. Investigations of the representation error show that optimally adjusted S-transform can also outperform the Wigner-Ville transform when dealing with time-frequency representations of the signal. The S- transform is also tested on nonstationary electric signals where it shows excellent tracking capability. These properties show that S-transform can be effectively used for analysis of electric signals, especially when dealing with multi-component time-varying waveforms

Manipulating Majorana Fermions in Quantum Nanowires with Broken Inversion Symmetry

We study a Majorana-carrying quantum wire, driven into a trivial phase by breaking the spatial inversion symmetry with a tilted external magnetic field. Interestingly, we predict that a supercurrent applied in the proximate superconductor is able to restore the topological phase and therefore the Majorana end-states. Using Abelian bosonization, we further confirm this result in the presence of electron-electron interactions and show a profound connection of this phenomenon to the physics of a one-dimensional doped Mott-insulator. The present results have important applications in e.g., realizing a supercurrent assisted braiding of Majorana fermions, which proves highly useful in topological quantum computation with realistic Majorana networks.Comment: 5 pages, 3 figures, Supplementary Material is adde

Neural networks for real-time estimation of parameters of signals in power systems

Fast determination of parameters of the fundamental waveform of voltages and currents is essential for the control and protection of electrical power systems. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. New parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural networks principles, are proposed. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least absolute value, the minimax, the least-squares and the robust leastsquares criteria are developed and compared. The networks process samples of observed noisy signals (voltages or currents) and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithms and neural network realizations. The proposed methods seem to be particularly useful for real-time, high-speed estimation of parameters of sinusoidal signals in electrical power systems

Neural networks for real-time estimation of parameters of signals in power systems

The purpose of this paper is to present new algorithms and along with them new architectures of analogue neuron-like adaptive processors for online estimation of parameters of sinusoidal signals, which are distorted by higher harmonics and corrupted by noise. For steady-state conditions we have developed neural networks which enable us to estimate the amplitudes and the frequency of the fundamental component of signals. When estimating the basic waveform of currents during short circuits the exponential DC component distorts the results. Assuming the known frequency, we have developed adaptive neural networks which enable us to estimate the amplitudes of the basic components as well as the amplitudes and the time constant of a DC component. The problem of estimation of signal parameters is formulated as an unconstrained optimization problem and solved by using the gradient descent continuous-time method. Basing on this approach we have developed systems of nonlinear differential equations that can be implemented by analog adaptive neural networks. The solution of the optimization problem bases on some principles given by Tank and Hopfield [ 4 ] as well as by Kennedy and Chua. The developed networks contain elements which are similar to the adaptive threshold elements of the perceptron presented by Widrow

Application of higher-order spectra for signal processing in electrical power engineering

In power spectrum estimation, the signal under consideration is processed in such a way, that the distribution of power among its frequency is estimated and phase relations between the frequency components are suppressed. Higher order statistics and their associated Fourier transforms reveal not only amplitude information about a signal, but also phase information. If a non-Gaussian signal is received along with additive Gaussian noise, a transformation to higher order cumulant domain eliminates the noise. These are some methods for estimation of signal components, based on HOS. In the paper we apply the MUSIC method both for the correlation and the 4th order cumulant, to investigate the state of asynchronous running of synchronous machines and the fault operation of inverter-fed induction motors. When the investigated signal is distorted by a coloured noise, more exact results can be achieved by applying cumulants

Adaptive Neural Networks for Robust Estimation of parameters of Noisy Harmonic Signals

In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural network principles. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least-squares (LS), the total least-squares (TLS) and the robust TLS criteria are developed and compared. The networks process samples of observed noisy signals and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithm

Wind Generator Transients’ Computation using Prony Method

The impact of wind generation on the electrical system should be assessed to guarantee error free operation and good power quality indicia. In this paper switching transients within wind generation units have been analyzed. Transients were simulated and measured. A Prony model of the signal and a nonlinear regression method were applied to determine transients’ parameters for various operation modes of the wind generator. Both methods delivered quite satisfactory results, but the regression method was sensitive to local minima