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.