http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
오늘 본 자료
Extraction of Fundamental Component in Power Quality Application using Tunable-Q Wavelet Transform
G.Ravi Shankar Reddy,Rameshwar Rao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8
Application of a Tunable-Q Wavelet Transform based technique is proposed in this paper for the extraction of Fundamental frequency component in Power Quality Disturbances. The TQWT filters are designed to extract the fundamental frequency component from the complete voltage (or) current signal. This is achieved by tuning the Q-factor and redundancy of the wavelet by primarily investigating the presence of interharmonics near the fundamental frequency. To test the effectiveness of the proposed scheme, the system is verified with various Power Quality Disturbances as per IEEE standards encountered in power system are considered here.
G. Ravi Shankar Reddy,Dr.Rameshwar Rao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11
In this paper Application of an Empirical Wavelet Transform based technique is proposed to estimate time-varying PQ indices for accurate assessment of Power Quality Disturbances. The EWT approach mainly aims to extract the actual fundamental frequency component and disturbance components from any distorted signal. The empirical wavelet transform consists of two major steps: detect the Fourier supports, and build the corresponding wavelet accordingly to those supports; filter the input signal with the obtained filter bank to get the fundamental component and disturbance components. Since the extracted components contain only one frequency component, Hilbert transform is utilized to estimate the instantaneous frequency and amplitude information, from this information we can estimate time-varying PQ indices. The proposed method is employed to assess successfully all sorts of Power Quality Disturbances such as voltage sag, swell, interruption, transients, harmonics, spikes, notches etc. From the results we can say that the proposed method detects disturbance start time, end time, duration of existence and its content more accurately.