Power Quality Problem Classification Based on Wavelet Transform and a Rule-Based method

This paper describes a Wavelet Transform and
Rule-Based method for detection and classification of
various events of power quality disturbances. In this model,
wavelet Multi-Resolution Analysis (MRA) technique was
used to decompose the signal into its various details and
approximation signals, and unique features from the 1st , 4th
, 7th and 8th level detail are obtained as criteria for
classifying the type of disturbance occurred. These features
and together with the duration of disturbance of occurrence
obtained from 1st level of detail, they form the criteria for a
Rule-Based software algorithm for detecting different kinds
of power quality disturbances effectively. It is presented in
this paper that the choice of sampling frequency is
important since it affects the average energy profile of the
details and eventually may cause error in detection of power
quality disturbances. The model is tested by using
MATLAB toolbox. The simulation produces satisfactory
result in identifying the disturbance and proof that it is
possible to use this model for power disturbance
classification. Since the method can reduce the number of
parameters needed in classification, less memory space and
computing time are required for its implementation. Thus it
stands up to be a suitable model to be used in real time
implementation through a dsPIC- based embedded system.