TY - JOUR

T1 - On Biased Harmonic Signal Estimation: Application to Electric Power Grid Monitoring

AU - Ahmed, Hafiz

AU - Ushirobira, Rosane

AU - Efimov, Denis

PY - 2022/11

Y1 - 2022/11

N2 - 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 article utilizes a linear regression model of the signal to solve this interesting practical problem. A continuous-time 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.

AB - 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 article utilizes a linear regression model of the signal to solve this interesting practical problem. A continuous-time 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.

KW - Convergence

KW - Dynamic regressor extension and mixing (DREM)

KW - Estimation

KW - Frequency estimation

KW - Harmonic analysis

KW - Linear regression

KW - Noise measurement

KW - Power system dynamics

KW - fixed-time convergence

KW - frequency estimation

U2 - 10.1109/TCST.2022.3155322

DO - 10.1109/TCST.2022.3155322

M3 - Article

VL - 30

SP - 2743

EP - 2750

JO - IEEE Transactions Control Systems Technology

JF - IEEE Transactions Control Systems Technology

SN - 1063-6536

IS - 6

ER -