In this paper, the simulation of the disturbance propagation through a large power system is performed on the WSCC 127 bus test system. The signal frequency analysis from several parts of the power system is performed by applying the Wavelet Transform (WT). The results show that this approach provides the system operators with some useful information regarding the identification of the power sy...

This paper presents a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process (GP) model. The GP prior model is trained by artificial bee colony algorithm. The nonlinear function of the objective system is estimated as the predictive mean function of the GP, and the confidence measure of the estimated nonlinear function is given by the predictive covariance...

In this paper we present an algorithm for continuous-time model identification from sample data using the weighted power moments of the output signal of a linear, time-invariant system. While most of the latest methods used in identification utilize a discrete-time model, the moments method is an alternative approach to directly identify a continuous-time model from discrete-time data. The meth...

Abstract Accurate and timely identification of power system operation states are requisite for taking control strategies ensuring security. With the high grid penetration renewable energy generation wide application electronic equipment, is becoming more complicated facing uncertainties. Traditional model based state methods faced with challenges data driven have great prospects. This paper pro...

Growing load demands, complex operating conditions, and the increased use of intermittent renewable energy pose great challenges to power systems. Serious consequences can occur when the system suffers various disturbances or attacks, especially those that might initiate cascading failures. Accurate and rapid identification of critical transmission lines is helpful in assessing the system vulne...