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Wavelet-based techniques for classification of power quality disturbances


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Hoang, TA 2003 , 'Wavelet-based techniques for classification of power quality disturbances', PhD thesis, University of Tasmania.

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The quality of power supply has become an important issue for electricity utilities
and their customers. In recent years there has been a rising incidence of damage
attributed to the power quality supplied to the customers of electric utilities.
Meanwhile, there has been a rapid increase in the already widespread use of
electronic equipment and modem power electronic devices. These trends have both
decreased the quality of power on the electric grid and increased the equipment's
sensitivity to power quality disturbances.
In order to improve the quality of the power supply, identifying the type and source
of troublesome disturbances is an essential task. Existing automatic disturbance
classification methods have replaced the traditional visual inspection of the
disturbance waveforms. However, they are not reliable because those methods rely
on the classification capability of large neural networks operating on inputs derived
by simply pre-processing the disturbance signals with discrete wavelet transforms
[134,135,136,137,138]. Long and redundant feature vectors both take a long time to
train the network and result in a reduced classification rate. In this thesis, we aim to
develop an efficiency method that automatically classifies power quality disturbances
by using wavelet transform techniques to generate short and nonredundant feature
Because of the wide range of power quality disturbances and their characteristic
waveforms, ranging from very simple stationary and deterministic harmonics to highly transient and stochastic waveforms, different and appropriate analysis
techmques are needed to achieve the overall classification objective. It is well
known that the traditional Fourier analysis is ideal for analysing steady state signal.
Although it is very powerful, Fourier analysis does not have the temporal resolution
needed to cope with sharp changes and discontinuities in signals.
Recent years have witnessed a proliferation in the applications of wavelet transforms
to signal analysis in a wide variety of fields, from geo-physics to telecommunications
to bio-medical engineering. This has occurred because wavelet analysis provides
dual localisations in both the time and the frequency domains. Moreover, wavelet
analysis allows the flexibility of choosing a wavelet that suits a particular
application. Especially by using the simple and flexible lifting scheme, we can
construct a time-variant or space-variant wavelet - known as second-generation
wavelet. The second-generation wavelet analysis makes optimal use of the
correlation between neighbouring signal samples and between neighbouring
frequency components to construct 'local' wavelets, which adapt to the local
characteristics of the signal. Common types of wavelet schemes are the orthonormal or biorthonormal wavelet
transforms that are typically used in compression and coding applications. This is
due to the fact that those schemes can be implemented with fast algorithms and they
are non-redundant representations of a signal. Unfortunately, they suffer the
limitation of not being translation invariant; a totally different set of transformed
coefficients is obtained when the same signal is shifted. This is the major concern in
pattern recognition applications.
There exist a number of wavelet schemes that have the shift invariance property in
their multiresolution representations. In this thesis, the local maxima and the
matching pursuit techniques are presented as the two most appropriate techniques for
power quality solutions. This is because the two techniques can efficiently
decompose a signal and have the ability to precisely measure power quality disturbance characteristics so that they represent the disturbances by a compact,
time-invariance feature vector.
The final task of classification is the selection of an appropriate classifier for use
with the feature vector. There are two main approaches of pattern recognition: one is
parametric and the other is non-parametric [129]. Parametric approaches can be
either deterministic or statistical. The statistical parametric approach requires a good
assumption about the statistical distribution of the data. On the other hand, the nonparametric
approach, known as the neural network approach, does not require any
statistical assumption about the data. In our statistical approach, we use a two-layer
network structure with locally tuned nodes in the hidden layer, known as Radial
Basis Function (RBF) network [106,120,121]. The network has only a local learning
capability and a limited learning inference from the training data, but trains quickly
as the training of the two layers is decoupled. In an RBF network, the crucial concern is the selection of cluster centres and their
widths. However, current techniques give suboptimum positions of cluster centres
and their widths, thus limiting the classification rate. To improve the p~rformance of
an RBF network, we propose to modify the structure of the RBF network by "'
introducing the weight matrix to the input layer (in contrast to the direct connection
of the input to the hidden layer of a conventional RBF) so that the training space in
the RBF network is adaptively separated by the resultant decision boundaries and
class regions. During training iterations, cluster centres, their widths and the input
layer weights are optimally determined together and concurrently adjusted to
maximise the discriminant between classes, thus minimising the classification error.
In this way the network has the ability to deal with complicated problems, which
have a high degree of interference in the training data, and achieves a higher
classification rate over the current classifiers using RBF.
For the classification of different types of disturbances that may be present on a
power supply, in this thesis we show that our automatic classification techniques
achieve superior recognition rates over current techniques. This improvement is done in two steps. The first improvement is the extraction of disturbance features
using appropriate signal processing tools from which we obtain an efficiency and
translation invariant feature vector. The second improvement is the designing of an
appropriate classifier which maximises the inter-class discriminant function.

Item Type: Thesis - PhD
Authors/Creators:Hoang, TA
Keywords: Electric power distribution, Wavelets (Mathematics)
Copyright Holders: The Author
Copyright Information:

Copyright 2003 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s)

Additional Information:

Chapter 7 is, in part, the equivalent of a pre-print of a paper accepted by IEE Electronic letters and is subject to Institution of Engineering and Technology Copyright. The final version has been published, the copy of record is available at IET Digital Library, published as T.A. Hoang and D.T. Nguyen, "Optimal learning for patterns classification in RBF networks," IEE Electronic Letters, vol. 38, no. 20, pp. 1188 -1190, Sep.

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