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- W2364354023 abstract "A new approach to recognize power quality disturbances is proposed.Based on fast Fourier transform(FFT) combined with dynamic measure method three kinds of features in power quality disturbance signals are extracted and using S-transform four features in power quality disturbance signals are extracted,and by use of decision tree and support vector machine(SVM) a combination classifier is designed.Firstly,for disturbance types with evident harmonic frequencies in FFT spectrum the features of main frequency points in FFT spectrum are extracted by the extreme point-enveloped dynamic measure method,and combining with the features extracted by S-transform,the disturbance types are preliminarily classified into several categories,and then by use of the two features extracted by S-transform the follow-up classification can be implemented.During the classification of decision tree the SVM is used to distinguish voltage sag from voltage interruption,thus the trouble that the feature thresholds,which vary with signal-to-noise ratio(SNR),are hard to be determined can be overcome.Simulation experiments show that using the proposed method eleven power quality disturbance signals,including two kinds of compound disturbances,can be accurately recognized,and when SNR is lowered to 20 dB the recognition accuracy can still reach to 96.50%.Comparison of the obtained results with reported classification results shows that the proposed method is accurate,stable and can be utilized in environment of low SNR." @default.
- W2364354023 created "2016-06-24" @default.
- W2364354023 creator A5083210367 @default.
- W2364354023 date "2013-01-01" @default.
- W2364354023 modified "2023-09-26" @default.
- W2364354023 title "Power Quality Disturbance Identification Using Decision Tree and Support Vector Machine" @default.
- W2364354023 hasPublicationYear "2013" @default.
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