Combination of Kalman filter and least error square techniques in power system


Autoria(s): Agha Zadeh, Ramin; Ghosh, Arindam; Ledwich, Gerard
Data(s)

01/10/2010

Resumo

An algorithm based on the concept of combining Kalman filter and Least Error Square (LES) techniques is proposed in this paper. The algorithm is intended to estimate signal attributes like amplitude, frequency and phase angle in the online mode. This technique can be used in protection relays, digital AVRs, DGs, DSTATCOMs, FACTS and other power electronics applications. The Kalman filter is modified to operate on a fictitious input signal and provides precise estimation results insensitive to noise and other disturbances. At the same time, the LES system has been arranged to operate in critical transient cases to compensate the delay and inaccuracy identified because of the response of the standard Kalman filter. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations and a laboratory test are presented to highlight the usefulness of the proposed method. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/37940/

Publicador

IEEE

Relação

http://eprints.qut.edu.au/37940/2/37940.pdf

DOI:10.1109/TPWRD.2010.2049276

Agha Zadeh, Ramin, Ghosh, Arindam, & Ledwich, Gerard (2010) Combination of Kalman filter and least error square techniques in power system. IEEE Transactions on Power Delivery, 25(4), pp. 1-13.

Direitos

Copyright 2010 IEEE

Fonte

Faculty of Built Environment and Engineering; School of Engineering Systems

Palavras-Chave #090607 Power and Energy Systems Engineering (excl. Renewable Power) #090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells) #090609 Signal Processing #Amplitude Estimation #Frequency Estimation #Harmonics #Kalman Filtering #Least-error Square
Tipo

Journal Article