A New Method With Hilbert Transform and Slip-SVD-Based Noise-Suppression Algorithm for Noisy Power Quality Monitoring

This paper presents a new method for automatic monitoring of noisy power quality, which is based on the Hilbert transform (HT) and the proposed slip-singular value decomposition (SVD)-based noise-suppression algorithm. The proposed method first employs the fast Fourier transform (FFT)-based low-pass filter and HT to obtain the instantaneous fundamental amplitude and the FFT sequence of the signal. Second, the slip-SVD-based noise-suppression algorithm and threshold filtering are used to extract cleaned singular value characteristic waveform of the high-frequency signal. Through judging the instantaneous fundamental amplitude, cleaned singular value characteristic waveform, and the FFT sequence, the presence of disturbances including single and combined disturbances can be easily detected by the proposed method. To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation disturbances and the actual data obtained from the practical power systems of China. The test results show that the proposed method has the advantages such as low false detection rate, good noise tolerance capability, short computational time, fewer parameters, practicability, and compatibility in comparison with the traditional disturbance detection methods. Besides, the proposed method can provide some important features such as amplitude, duration, and frequency for classification. Such advantages make the proposed method to be a good choice for real-time applications.