Paper Info
Title
Fast Harmonic Estimation of Stationary and Time-Varying Signals Using EA-AWNN
Abstract
Field measurement of harmonic distortion is a fundamental requirement for monitoring, analysis, and/or control of power system harmonics. Fast and accurate estimation of time-varying harmonics is a key to realize many objectives of the smarter and cleaner grid such as harmonic source identification, improved active filter control for mitigation of harmonics, and smart meters for harmonic pollution metering. This paper presents a fast and accurate approach for real-time estimation of moderate time-varying harmonics of voltage/current signals. The proposed method is based on estimation of signal parameters via rotational invariance technique (ESPRIT)-assisted adaptive wavelet neural network (AWNN). The AWNN provides quick estimates (twice every fundamental cycle with only half-cycle data as input) of the dominant harmonics, whereas the ESPRIT complements it to handle time-varying signals with higher accuracy. The salient features of the proposed method are validated on the simulated and experimental signals of stationary and time-varying nature.
Year
DOI
Venue
2013
10.1109/TIM.2012.2217637
Instrumentation and Measurement, IEEE Transactions
Keywords
Field
DocType
active filters,harmonic distortion,learning (artificial intelligence),neural nets,pollution measurement,power engineering computing,power system harmonics,power system measurement,smart meters,EA-AWNN,ESPRIT-assisted adaptive wavelet neural network,dominant harmonics,fast harmonic estimation,harmonic distortion field measurement,harmonic pollution metering,harmonics mitigation,improved active filter control,power system harmonics analysis,power system harmonics control,power system harmonics monitoring,real-time estimation,rotational invariance technique-assisted adaptive wavelet neural network,signal parameters estimation,smart meters,stationary signals,time-varying harmonics,time-varying signals,voltage-current signals,Adaptive learning,adaptive wavelet neural network (AWNN),estimation of signal parameters via rotational invariance technique (ESPRIT),interharmonics,power quality,total harmonic distortion
Rotational invariance,Active filter,Total harmonic distortion,Voltage,Harmonic,Control engineering,Electronic engineering,Harmonics,Artificial neural network,Metering mode,Mathematics
Journal
Volume
Issue
ISSN
62
2
0018-9456
Citations 
PageRank 
References 
13
0.79
12
Authors
2
Name
Order
Citations
PageRank
Sachin K. Jain1504.61
S. N. Singh29913.38