SPLIT-ADC BASED DIGITAL BACKGROUND CALIBRATION FOR TIME-INTERLEAVED ADC

A novel Time-Interleaved Analog-to-Digital Converter (TIADC) digital background calibration for the mismatches of offsets, gain errors, and timing skews based on split-ADC is proposed. Firstly, the split-ADC channels in present TIADC architecture are designed to convert input signal at two different channel sampling rates so that redundant channel to facilitate pair permutation is avoided. Secondly, a high-order compensation scheme for correction of timing skew error is employed for effective calibration to preserve high-resolution when input frequency is high. Numerical simulation performed by MATLAB for a 14-bit TIADC based on 7 split-ADC channels shows that Signal-to-Noise and Distortion Ratio (SNDR) and Spurious Free Dynamic Range (SFDR) of the TIADC achieve 86.2 dBc and 106 dBc respectively after calibration with normalized input frequency near Nyquist frequency.

Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.