Robust Algorithm Development of Frequency Estimation in Smart Grid

Smart grid is an intelligent power generation system in modern electricity networks, with the commonly used model being the three-phase power system. Natural and equipment factors such as lightning, high-power equipment start and stop, switch on and off, and corona discharge of high-voltage transmission lines may produce sudden strong, short duration, high instantaneous power pulse noise in the power system. This paper addresses robust parameter estimation in the presence of impulsive noise. After converting the three-phase waveforms into a pair of orthogonal signals via-transformation, an-norm based robust estimator is developed to accurately find the frequency, phase, and voltage parameters. A new adaptive filtering algorithm, Improved ACLMS (IACLMS), was developed by updating filter coefficients using the-th power of estimation error as cost function. Computer simulations demonstrate that compared with the ACLMS algorithm, the IACLMS algorithm has stronger robustness and faster convergence speed, but slightly lacks precision in frequency estimation. Therefore, based on this method, a nonlinear function relationship between the step size factor and the estimation error was established, and a new variable step size IACLMS (VSS-IACLMS) algorithm was obtained. This method improves the frequency estimation accuracy compared to the IACLMS algorithm, and its mean square error performance can reach the Cramér-Rao lower bound.