Abstract
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.
Highlights
Electric power has become an indispensable part of national life, and improving the power quality (PQ) conditions has great significances in the normal operations for power grids
The feature statistics for all the noisy signals are analyzed, the comparison results are depicted in Figure 6 (d), whereas the results indicate that the statistics for noisy PQ signals do not display an obvious deviation in terms of value distributions
In this paper, a PQ disturbance recognition method based on the Multiresolution S transform (MST) and decision tree (DT) is proposed
Summary
Electric power has become an indispensable part of national life, and improving the PQ conditions has great significances in the normal operations for power grids. With the development of modern electronic technology, a large number of unbalanced non-linear loads and new energy with random fluctuation characteristics have been added to the power grids, resulting in many PQ disturbance events, such as harmonic and transient disturbances [1]–[3]. These disturbance events have negative impacts on the performances for the equipment based on precision computers and microprocessors, in some conditions, even bring some unexpected consequences [4].
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