Computation of spectral components in system with wind generation unit

Signal parameters estimation is an important prerequisite for assessment of power quality (PQ) indices. Nowadays, large amounts of measured data need to be automatically processed for appropriate and useful data mining in PQ. Especially, modern wind generators are often seen as sources of PQ disturbances, which should be constantly supervised. The authors propose an application of modified Singular Value Decomposition (SVD) method for signal parameters estimation. Results of the proposed method are compared with broadly used Fourier Transform. Additionally,, results from Prony method are presented. A mechanical model of doubly fed induction generator (DFIG), operating in various conditions was chosen as a source of disturbed signals. Research results verify the usefulness of SVD based method

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

Nonlinear regression applied for power quality disturbances characterization in grids with wind generators

The impact of wind generation on the electrical system should be assessed to figure out potential hazards to system operation and deterioration of power quality indices. In this paper signal processing algorithms has been applied to analyse switching transients within wind generation units. Nonlinear regression method and Prony model 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 prone to local minima

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

Dissipation-driven superconductor-insulator transition in linear arrays of Josephson junctions capacitively coupled to metallic films

We study the low-temperature properties of linear Josephson-junction arrays capacitively coupled to a proximate two-dimensional diffusive metal. Using bosonization techniques, we derive an effective model for the array and obtain its critical properties and phases at T = 0 using a renormalization group analysis and a variational approach. While static screening effects given by the presence of the metal can be absorbed in a renormalization of the parameters of the array, backscattering originated in the dynamically screened Coulomb interaction produces a non-trivial stabilization of the insulating groundstate and can drive a superconductor-insulator transition. We study the consequences for the transport properties in the low-temperature regime. In particular, we calculate the resisitivity as a function of the temperature and the parameters of the array, and obtain clear signatures of a superconductor-insulator transition that could be observed in experiments.Comment: 10 pages, 5 figures, submitted to Physical Review