Classification of power quality disturbance using segmented and modified S-transform and DCNN-MSVM hybrid model

In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustab...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Liu, Mingping, Chen, Yue, Zhang, Zhen, Deng, Suhui
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
classification
Computer simulation
Contours
deep convolutional neural network
Deep learning
Disturbances
Feature extraction
Frequency conversion
Gaussian window functions
multiclass support vector machine
Noise levels
Noise measurement
Power quality disturbance
segmented and modified S-transform
Signal classification
Signal quality
Signal resolution
Support vector machines
Time-frequency analysis
Transformations (mathematics)
Waveforms
Window functions
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Summary:In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustable parameters was used in the Gaussian window function. The accurate time-frequency localization and efficient feature extraction of different PQ disturbances then could be achieved. Firstly, the SMST was used to analyze the PQ disturbance signals and obtained two-dimensional (2D) contour maps with high time-frequency resolution. Then, the DCNN was employed to automatically extract features from the 2D contour maps. Finally, the MSVM classifier was developed for the classification of single and complex signals of PQ disturbance. In order to demonstrate the effectiveness and robustness of the proposed model, eight single and thirteen complex waveforms of PQ disturbance were considered without noise and with different noise level, respectively. Extensive simulations are performed and compared to other existing methods. The simulation results show that the proposed method has better performance than several state-of-the-art algorithms in classifying PQ disturbances under different noise level.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3233767