期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 14, 期 19, 页码 4010-4020出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2019.1678
关键词
feature extraction; Kalman filters; power supply quality; transforms; feedforward neural nets; power engineering computing; signal denoising; signal classification; control engineering computing; power system control; waveform analysis; power quality disturbance classification; KF-ML-aided S; multilayer feedforward neural networks; PQ control; Kalman filter; maximum likelihood; PQ disturbances; original distorted waveform; ST; feature extraction; KF-ML-aided S-transform; noise figure 20; 0 dB
资金
- National Science Foundation of China [51507015, 51577014, 51577013, 51877012]
- Hunan Province Natural Science Foundation of China [2018JJ2439, 2015JJ3008]
- Education Bureau of Hunan Province, China [18B130]
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.
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