NDLI: Classification of power quality disturbances based on KF-ML-aided S-transform and multilayers feedforward neural networks

Content Provider	IET Digital Library
Author	Xi, Yanhui Li, Zewen Tang, Xin Zeng, Xiangjun
Abstract	Classifying power quality (PQ) disturbances is one of the most important issues for PQ control. The S-transform (ST)-based neural networks in conjunction with Kalman filter based on maximum likelihood (KF-ML) are presented for classification of PQ disturbances. To accurately extract features in high-noise cases, the KF-ML is used to remove noise from the original distorted waveform. Then, ST technique is used to extract the significant features of disturbances. Finally, a classifier based on multilayers feedforward neural networks can accurately recognise various types of PQ disturbances. Six simulated single disturbances and six complex ones with different noise levels are tested for the sensitivity to noise. Classification results show that the classification accuracy of the proposed method is more than 95% even in 20 dB high-noise condition, and also validate the superiority of strong rejection to noises. Comparison studies between the proposed method and other classification methods are also reported to show the advantages of the proposed approach.
Starting Page	4010
Ending Page	4020
Page Count	11
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 19, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1678
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-07-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Control Engineering Computing Control of Electric Power System Digital Signal Processing Feature Extraction Feedforward Neural Network Filtering Method in Signal Processing Harmonics Integral Transforms Kalman Filter KF-ML-aided S KF-ML-aided S-transform Mathematical Analysis Maximum Likelihood Multilayer Feedforward Neural Network Neural Computing Technique Noise Figure 20.0 DB Original Distorted Waveform Power Engineering Computing Power Quality Disturbance Classification Power Supply Quality Power System Control PQ Control PQ Disturbances Signal Classification Signal Denoising Transform Waveform Analysis
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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