Analysis of nonstationary power-quality waveforms using iterative hilbert huang transform and sax algorithm

Citation data:

IEEE Transactions on Power Delivery, ISSN: 0885-8977, Vol: 28, Issue: 4, Page: 2134-2144

Publication Year:
2013
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Citations 36
Citation Indexes 36
Repository URL:
http://ro.uow.edu.au/eispapers/1654
DOI:
10.1109/tpwrd.2013.2264948
Author(s):
Afroni, Mohammad Jasa; Sutanto, Darmawan; Stirling, David
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Energy; Engineering; nonstationary; algorithm; analysis; sax; transform; huang; hilbert; iterative; waveforms; quality; power; Science and Technology Studies
article description
The nonstationary nature of power-quality (PQ) waveforms requires a tool that can accurately analyze and visually identify the instants of transitions. One of the recently reported tools available to analyze nonstationary complex waveforms with a very good time resolution is the Hilbert Huang Transform (HHT). However, HHT has difficulty in resolving waveforms containing components with close frequencies with a ratio of less than two, and similar to other waveform classification techniques, it has difficulty in resolving the instants of sudden changes in the waveform. To overcome the problem with components containing close frequencies, a novel Iterative Hilbert Huang transform (IHHT) is proposed in this paper. To overcome the problem in identifying instants of sudden changes in the waveform, a new method using the Symbolic Aggregate ApproXimation (SAX) method is proposed. SAX converts the signal into symbols that can be utilized by a pattern detector algorithm to identify the boundaries of the stationary signals within a nonstationary signal. Results from IHHT and SAX method to analyze and visually identify simulated and measured nonstationary PQ waveforms will be provided and discussed. The proposed method is particularly useful when investigating the behavior of the harmonic components of a particular PQ waveform of interest in a given interval of time, following a data-mining search of a large database of PQ events. It can be used to both identify, and later isolate unique signatures within a provider's distribution system. © 1986-2012 IEEE.