Power quality disturbances classification using rotation forest and multi-resolution fast S-transform with data compression in time domain

Considering the high spatial complexity of signal processing methods and the low diversity between the base classifiers in the ensemble classifier, a new method with optimal multi-resolution fast S-transform (OMFST) with low spatial complexity and rotation forest (ROF) was proposed. Firstly, Gini importance of features is evaluated by random forest (RF), and the sequence forward search method was adopted for feature selection. Then, the intermediate matrix was constructed on the optimal feature set. The results of inverse fast Fourier transform in the main frequency points of signals based on OMFST are compressed in time domain. The intermediate matrix was used to extract features for power quality (PQ) disturbances recognition. Finally, the ROF was adopted to encourage simultaneously individual accuracy and diversity within the ensemble. The optimal ROF was applied to identify 17 kinds of PQ disturbance signals. The simulation results show that the new method can effectively compress the time-frequency matrix of the existing S-transform (ST) method; the space complexity of the ST modular matrix is reduced significantly and has higher accuracy. Besides, the results of the experiment with real PQ data prove that the new method was effective for practical industrial applications.