A Gauss–Newton ADALINE for Dynamic Phasor Estimation of Power Signals and Its FPGA Implementation

Abstract

This paper presents a new hybrid adaptive filter based on modified Gauss–Newton adaptive linear element (MGNA) for estimating the fundamental and harmonic phasors along with the frequency change of nonstationary power system signals useful in many application areas that include system control, digital relaying, state estimation, and also wide area systems. The proposed approach is used to minimize an objective function based on weighted square of the error using the MGNA. Moreover, the inverse of the Hessian matrix is computed assuming certain approximations to reduce the computational load and time consumption. Furthermore, it also uses recursive formulation using the estimated values from the previous time instant unlike the nonrecursive approaches, thereby exhibiting better performance in terms of accuracy and convergence. Besides, its simple structure makes it more suitable for real-time applications. In addition, the filter has been implemented on a field programmable gate array hardware and Xilinx 14.2 with the Sysgen software for the estimation of frequency, fundamental, and harmonic phasors of single and three-phase time-varying power system signals.