An Extensible Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network

Accurate parameter identification of power distribution network (PDN) has attracted remarkable attention recently. However, power device parameters usually show an instability attributed to both the operating status and manual entry. Therefore, it is urgent to develop reliable algorithms for identifying PDN parameters with both high accuracy and high efficiency. Most of the existing algorithms are gradient-free and based on the heuristic schemes, resulting in an unstable numerical calculation. Herein, based on our previous work about the adaptive gradient-based optimization (AGBO) method, we propose an extensive version, namely, AGBO-Pro model. In this method, both the numerical and categorical features of experimental observations are utilized and incorporated with each via a weighted average. By comparing the proposed method with several heuristic algorithms, it is found that the errors in RMSE, MAE, and MAPE criteria via AGBO-Pro are all about 2 times lower with a much faster and more stable convergence of the loss function. By further taking a linear transformation of the loss function, the AGBO-Pro model achieves a more robust performance with a much lower variance in repeat numerical calculations. This work shows great potential in possible extension of gradient-based optimization methods for parameter identification in PDN.