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Studies of power quality : disturbance recognition

Ringrose, MJ (2003) Studies of power quality : disturbance recognition. PhD thesis, University of Tasmania.

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Abstract

Power quality is becoming increasingly important in power systems. The proliferation of
modem power electronic devices has both decreased the quality of power on the electric
grid and increased the equipment's sensitivity to power quality disturbances. In order to
maintain an acceptable level of power quality in a power system, power quality
monitoring devices have been designed. These devices are purchased by power utilities
and customers in increasing numbers for the purpose of power quality trend logging and
power quality disturbance capturing. Many power quality monitors will provide some
information on the type of a power quality disturbance that has been recorded, however a
reliable system for automatically classifying the full range of disturbance types is yet to
be formalised. Recent advances in signal processing and artificial intelligence have put
this goal within reach.
A system for the automatic classification of recorded power quality disturbances is
developed in this thesis. The system uses a variety of mathematical tools in order to
produce an accurate and robust classification method. These tools include Fourier
transforms, wavelet transforms, artificial neural networks, and fuzzy logic. Transient
disturbances are analysed using the wavelet transform general modulus maxima
technique and classified into their respective disturbance classes using a neuro-fuzzy
pattern recognition scheme. The remaining disturbance types are analysed using Fourier
transforms and classified into their respective disturbance classes using a simple decision
making scheme.
Also investigated in this thesis is a method for monitoring the harmonic output from nonlinear
loads in a power system using a reduced number of harmonic monitoring stations.
The method utilises a state estimation scheme with measurements from installed
harmonic monitoring stations combined with pseudo-measurements provided by artificial
neural networks.

Item Type: Thesis (PhD)
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Date Deposited: 08 Aug 2011 04:01
Last Modified: 11 Mar 2016 05:54
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