Numerical calculation of varistor model for sinusoidal signal

ZnO varistors are semiconductor devices with highly nonlinear current-voltage characteristic and are widely used as devices for overvoltage protection. Varistor applications range from the use of small varistors to protect electronic components to large varistors for protection of power systems. This paper presents proposed model of ZnO varistor and methodology of its mathematical analysis and simulation. The mathematical analysis of the proposed model makes it possible simulate the current trace on a nonlinear element

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

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

Water safety plans and climate change mitigation

[Excerpt] Definition Quality water at affordable prices for all is a key condition for the promotion of public health, environmental sustainability, and quality and safety of life. In a context of growing external uncertainties arising from changes in the climate and the environment, ensuring these conditions is an upward concern and is of utmost relevance to increase scientific research on the impacts of climate change on water quality modification and in minimization/mitigation strategies

Adaptacyjne sieci neuronowe w zastosowaniu do identyfikacji stanu układów elektrycznych

Celem pracy jest zbadanie możliwości zastosowania sieci neuronowych do estymacji parametrów sygnałów w czasie rzeczywistym, których adaptacyjny algorytm działania oparty jest na nowych kryteriach TLS i RTLS oraz zbadanie możliwości zastosowania wybranych układów sieci neuronowych do identyfikacji stanu pracy niektórych układów elektrycznych. T e z a p r a c y: Adaptacyjne sieci neuronowe umożliwiają dokładniejszą oraz szybszą identyfikację niektórych stanów pracy układów elektrycznych, aniżeli dotychczas stosowane metody

Numerical calculation of varistor model for sinusoidal signal

ZnO varistors are semiconductor devices with highly nonlinear current-voltage characteristic and are widely used as devices for overvoltage protection. Varistor applications range from the use of small varistors to protect electronic components to large varistors for protection of power systems. This paper presents proposed model of ZnO varistor and methodology of its mathematical analysis and simulation. The mathematical analysis of the proposed model makes it possible simulate the current trace on a nonlinear element

Artificial Neural Network for Real-Time Estimation of Basic Parameter of Signals

This paper presents for students instructions to using parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural networks principles for estimation of parameters of signals in power system. Algorithms based on the standard least-squares (LS) criteria is proposed. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent optimization algorithm. The corresponding architectures of analogue neuron-like adaptive processors are also shown

Methods of Determining Internal Parameters of Varistor Model

ZnO varistors are semiconductor devices with highly nonlinear current-voltage characteristic and are widely used as devices for overvoltage protection. Varistor applications range from the use of small varistors to protect electronic components to large varistors for protection of power systems. This paper presents proposed model of ZnO varistor and methodology of its mathematical analysis and simulation. The mathematical analy-sis of the proposed model makes it possible simulate the current trace on a nonlinear element