Real-time harmonics estimation in power systems using a novel hybrid algorithm

This study presents a new hybrid algorithm to estimate the harmonic parameters of a distorted signal in power systems. The parameters to be estimated are amplitudes and phases of harmonic components according to the voltage/currents samples. The proposed algorithm is based on combination of the recu...

Full description

Saved in:
Bibliographic Details
Published in:IET generation, transmission & distribution transmission & distribution, 2017-09, Vol.11 (14), p.3532-3538
Main Authors: Enayati, Javad, Moravej, Zahra
Format: Article
Language:English
Subjects:
amplitude estimation
C++ code
convergence
discrete Fourier transform
discrete Fourier transforms
distorted signal
dynamic parameter estimation
ensemble Kalman filter
harmonic components
harmonic parameters
intended state vector
intensive noise presence
iterated extended Kalman filter
Kalman filters
least squares approximations
linear amplitude estimation
MATLAB
nonlinear filters
nonlinear phase estimation
novel hybrid algorithm
PC/104 computer set
phase estimation
power supply quality
power system harmonics
power system parameter estimation
power systems
practical power quality
real‐time harmonics estimation
recursive least squares
Research Article
RLS–IEKF algorithm
tuning factor
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study presents a new hybrid algorithm to estimate the harmonic parameters of a distorted signal in power systems. The parameters to be estimated are amplitudes and phases of harmonic components according to the voltage/currents samples. The proposed algorithm is based on combination of the recursive least squares (RLS) and iterated extended Kalman filter (IEKF) techniques. The RLS–IEKF algorithm decomposes the problem into linear amplitude estimation and non-linear phase estimation leading to extracting the intended state vector in online mode and intensive noise presence. As well, RLS–IEKF estimates dynamic parameters using tuning factor which controls the impact of measurement on estimation process. Simulation results obtained by MATLAB show the accuracy and speed of convergence in comparison with that of conventional discrete Fourier transform and ensemble Kalman filter. For further validation, the proposed algorithm is implemented by C++ code and is applied to real switching current data. The real-time implementation of RLS–IEKF in a simple laboratory setup using PC/104 computer set and dedicated hardware shows its satisfactory performance for practical power quality and protection cases.
ISSN:1751-8687
1751-8695
1751-8695
DOI:10.1049/iet-gtd.2017.0044