APPLICATION OF ABRUPT CHANGE DETECTION-BASED
SIGNAL SEGMENTATION IN POWER SYSTEM OSCILLATION
A Ukil*, R Živanović*
*Tshwane University of Technology, Private Bag X680, Pretoria, 0001, South Africa.
Abrupt change detection-based signal segmentation has significant role to play in automatic
segmentation of signal. The segmented signal can be used for automated analysis and effective further processing.
Abrupt change detection-based automatic signal segmentation has been effectively utilized for automatic
disturbance recognition in South Africa. In this paper, we describe the application of the abrupt change detectionbased automatic signal segmentation ...view middle of the document...
In the scope of this paper, we describe the application
of the abrupt change detection-based automatic
signal segmentation for the analysis of the power
oscillation signals obtained from the Mexican
interconnected system (MZD-DGD) . We present
the applications results by applying our segmentation
algorithms to the test signals for the inter-area
oscillation in the MZD-DGD, acquired in
collaboration with the Mexican team.
ABRUPT CHANGE DETECTION
Detection of abrupt changes in signal characteristics
is a much studied subject with many different
approaches. A possible approach to recognitionoriented signal processing consists of using an
automatic segmentation of the signal based on abrupt
changes detection as the first processing step. Many
of these signals are quasi-stationary, that is, the
signals are composed of segments of stationary
behavior with abrupt changes in their characteristics
in the transitions between different segments. It is
imperative to find the time-instants in which the
changes occur and to develop models for the
different segments during which the system does not
change. A segmentation algorithm splits the signal
into homogeneous segments, the lengths of which are
adapted to the local characteristics of the analyzed
signal. This can be achieved either on-line or off-line
Assuming a parametric system model, we consider a
quasi-stationary sequence of k independent observations x , with a d-dimensional parameter vector
θ which describes the properties of the observations.
Before the unknown change time t 0 , the parameter θ
is equal to θ 0 , while after the change, it is equal to
θ 1 ≠ θ 0 . At this stage, two tasks are necessary: to
detect the change time-instant t 0 and to estimate the
corresponding parameter vectors θ 0 and θ 1 . With the
primary focus on detecting the change time-instant
t 0 , it is useful to consider t 0 a random unknown
variable with unknown distribution .
To accomplish the abrupt change detection, hence
segmentation of the power system disturbance signals, the following algorithms are considered.
Recursive Identification Method 
Wavelet Transform Method 
Adaptive Whitening Filter and Wavelet
Adjusted Haar Wavelet Method 
Complete Algorithm .
In Fig 1, we show an application result for the
segmentation of a fault signal obtained from the
ESKOM digital fault recorders (DFRs) implemented
using MATLAB, based on the wavelet transform
Proceedings of the 15th Southern African Universities Power Engineering Conference
Fig 2: Active Power Flow Oscillations in the Mexican Interconnected System (MZD-DGD).
Fig 1: RED Phase Current Signal Segmentation.
In Fig 1, the original DFR recording for current
during the fault in the RED-Phase is shown in the top
section, wavelet coefficients for this fault signal (in