Accurate parameter estimation for unbalanced three-phase system.

Yuan Chen,Hing Cheung So

doi:10.1155/2014/539420

Yuan Chen, Hing Cheung So

Open Access

https://doi.org/10.1155/2014/539420

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Journal: TheScientificWorldJournal	Publication Date: Jan 1, 2014
Citations: 11	License type: CC BY 3.0

Affiliation: University of Hong Kong

Abstract

Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS.

Highlights

Due to the increasing demand for electricity and the finite supply of nonrenewable energy sources, electrical power generation systems have faced a huge challenge
Traditional grid is a star network including a central point and leaf nodes, whereas the smart grid is a mesh network whose nodes can act as both users and generators
Conventional estimators exhibit poor behavior applied for the three-phase system directly, because they work well only for the single-phase signal, which cannot truly characterize the unbalanced system [6]

Summary

Introduction

Due to the increasing demand for electricity and the finite supply of nonrenewable energy sources, electrical power generation systems have faced a huge challenge. By making use of the αβ-transformation [7], the three-phase waveforms can be mapped into a pair of in-phase and quadrature signals. Based on this model, a number of approaches for frequency estimation have been proposed, including the complex least mean square (CLMS) [8] and augmented complex least mean square (ACLMS) [9] methods. A number of approaches for frequency estimation have been proposed, including the complex least mean square (CLMS) [8] and augmented complex least mean square (ACLMS) [9] methods Both estimators only focus on finding the frequency of the unbalanced system.

Proposed Method

Simulation Results

Conclusion