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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)
Additional Information: Copyright the Author
Date Deposited: 08 Aug 2011 04:01
Last Modified: 03 Oct 2012 06:34
URI: http://eprints.utas.edu.au/id/eprint/11455
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