Accurate device parameters play a critical role in the calculation and analysis of power distribution networks (PDNs). However, device parameters are always affected by the operating status and influenced by manual entry. Besides, the distribution area of PDN is very wide, which brings more challenges to parameter identification work. Therefore, developing appropriate algorithms for accurately identifying PDN parameters has attracted much more attention from researchers recently. Most of the existing parameter identification algorithms are gradient-free and based on heuristic schemes. Herein, an adaptive gradient-based method is proposed for parameter identification in PDN. The analytical expressions of the gradients of the loss function with respect to the parameters are derived, and an adaptive updating scheme is utilized. By comparing the proposed method and several heuristic algorithms, it is found that the errors in both three criteria via our solution are much lower with a much smoother and more stable convergence of loss function. By further taking a linear transformation of the loss function, the method of this work significantly promotes the parameter identification performance with much lower variance in repeat experiments, indicating that the proposed method in this work achieves a more robust performance to identify PDN parameters. This work gives a practical demonstration by utilizing the gradient-based method for parameter identification of PDN.

Accurate network parameters are of great importance for the accurate control of the power distribution network (PDN). In fact, the line parameters of the PDN are always affected by external operating conditions. However, most of the line parameters in the PDN account are static parameters. In order to obtain the dynamic parameters that reflect the line operating condition, this study presents a method that uses only the RMS voltage of the first section of the line and the RMS voltage and power at the low-voltage side of the transformer. This study introduces the processing method of abnormal measurement data, constructs a derivative-free identification equation represented by a matrix, and uses the designed loss function combined with a heuristic method to solve the equation. An actual feeder is used in the experimental part. The experimental data show that the method has some antipower noise ability, and the identification accuracy of this method is better than that of the genetic algorithm and random search algorithm.