  Abrupt change detection based on the wavelet transform and threshold method
is very effective in detecting the abrupt changes and hence segmenting the
signals recorded during disturbances in the electrical power network. The
wavelet method estimates the time-instants of the changes in the signal model
parameters during the pre-fault condition, after initiation of fault, after
circuit-breaker opening and auto-reclosure. Certain kinds of disturbance
signals do not show distinct abrupt changes in the signal parameters. In those
cases, the standard mother wavelets fail to achieve correct event-specific
segmentations. A new adjustment technique to the standard Haar wavelet is
proposed in this paper, by introducing 2n adjusting zeros in the Haar wavelet
scaling filter, n being a positive integer. This technique is quite effective
in segmenting those fault signals into pre- and post-fault segments, and it is
an improvement over the standard mother wavelets for this application. This
paper presents many practical examples where recorded signals from the power
network in South Africa have been used.
