On Biased Harmonic Signal Estimation: Application to Electric Power Grid Monitoring

Abstract

Parametric estimation of a biased harmonic signal is a significant technical challenge for many engineering applications. Such a problem is particularly important for electric utility grid-connected power electronic converters. This paper utilizes a linear regression model of the signal to solve this interesting practical problem. A continuoustime dynamic regressor extension and mixing (DREM) based approach is then applied for parameter estimation. For practical implementation, continuous-time estimators are discretized using implicit and explicit Euler methods. We then prove that the implicit discretization can achieve fixed-time convergence for the unknown frequencies estimation. Thanks to the estimated frequencies, another DREM-based linear regression problem is solved for the parameter estimation purpose. The overall order of the proposed technique is the same as the number of unknown parameters, making the estimator suitable for real-time implementation in embedded devices. Theoretical results are validated through extensive comparative experimental studies.

Fundamental frequency sequence amplitude estimator for power and energy applications

“…In the conventional DSC-based DC offset rejection approach, half-cycle delayed signals are used. Recently, in [ 31 , 38 , 39 ], an alternative approach has been presented where DC offset rejection can be done using an arbitrary length of delay. As such, the modified DSC approach is adopted in this work.…”

An Improvement of the Nonparametric Frequency Estimation Below Two Signal Periods Using Two Windows

“…In many cases, fast and robust real-time estimates with low measurement uncertainty are needed. Many frequency estimators have been proposed using sinusoidal regression in time or frequency space [6], but the basic division of methods is into parametric methods [7] and faster nonparametric methods based on the discrete Fourier transform (DFT). The use of parametric methods requires a good signal model [8] and the uncertainty of the evaluation is typically reduced iteratively, which costs a lot of computing time.…”

“…[4][5][6][7], the maximum errors of the frequency estimation|E max (θ )| = | ⌢ θ est − θ | (in bins, bin = f / f )are sought when a double scan was performed. The frequency was changed in the interval 0.8 < θ 1 < 1.2 with step θ 1 = 0.1, and at each frequency, the phase of the…”