Power line parameter identification based on a binary nonlinear regression algorithm

Various new energy, energy storage, and adjustable loads are incorporated into the distribution network to participate in the adjustment of the distribution network side, making the operation of the distribution network side more flexible. As an important part of the distribution network, the power lines of the distribution network are directly affected by this change. Therefore, the online detection of distribution network parameters in the distribution network becomes more and more important. Currently, smart devices can perfor15wm parameter estimation and topology recognition in a data-driven manner. However, many current methods require the use of phasor measurement units to measure the phase angle of the node voltage, which is not feasible for current relatively traditional power distribution networks. Based on the more accurate power detection of the current distribution network, this paper proposes a method for estimation of the line parameters of the distribution network without considering the voltage phase angle information. Based on the limited measurement data of the distribution network, the measured electricity is all amplitude. By modeling the transmission line of the distribution network, the problem of solving the transmission line parameters is transformed into a binary nonlinear regression problem. The binary nonlinear least-squares regression method is used to identify the transmission line parameters, and the residual analysis is used to eliminate anomalies data. After that, the reliability and accuracy of the method are analyzed for the identification results under different data volumes and different error levels. Finally, the line parameters within a day were identified under the condition of line temperature changes. By comparing the results of electrothermal coupling, the changes of line parameters in a day can be more accurately identified.