Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.