| Abstract: | Accurate classification of power quality disturbance is the premise and basis for improving and governing power
quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and
decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each
power quality disturbance signal, and a decision tree with classification rules is then constructed for classification
and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are
established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven
time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed
method can effectively identify six types of common single disturbance signals and two mixed disturbance signals,
with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of
support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses
S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy
for power quality disturbance. |
