The New Definitions of Loss Function for the Model-Based Parameter Identification Method in Power Distribution Network

Abstract

Accurate device parameters play critical roles in calculation and analysis of power distribution network (PDN). However, device parameters are always affected by the operating status and influenced by manual entry; besides, the distribution area of PDN is very wide with many points, which brings more challenges to PDN parameter identification. Most of the proposed algorithms recently assume that the parameters of PDN contribute in a nonlinear probability space and optimize parameters by the power flow model with a loss function. Although these algorithms can achieve satisfying results in PDN analysis, the relationship between the power flow model and loss functions remains unclear. In this paper, the outputs of the power flow model have been analyzed firstly by experimental data, which includes the head and end voltages, as well as active and reactive power on the low-voltage side. It is revealed that the loss functions used by current algorithms are not suitable and reasonable for power flow model in PDN calculation, which constitutes one of the main findings of this work. Subsequently, this work proposes four novel loss functions combined with genetic algorithm (GA) and Markov Chain Monte Carlo (MCMC) to identify PDN parameters. Compared with the published algorithms, our experimental results show that the loss function defined in this paper can achieve better and more stable performance with about two times lower in MAE, RMSE, and RMPE evaluation functions to identify PDN parameters.

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

“…e reason can be attributed to the fact that calculating mean square error of U c and U cal neglecting the statistical relationship between them and has a negative impact on parameter identification. erefore, based on the previous study [26], the line transformation can be implemented to U cal before calculating the loss function. e parameters of line transformation are optimized with other PDN parameters simultaneously, and their results are displayed in Table 3, Figures 8 and 9.…”

“…Raw Dataset Description. In this paper, 1499 samples in the raw dataset were collected by SCADA with a sampling period of 15 minutes [25,26]. e three-phase first section voltages on the high-voltage side (denoted as U a , U b and U c ) are shown in Figure 2, and the low-voltage sides of them (denoted as u a , u b , and u c ) are displayed in Figure 3.…”

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

“…Data Collection and Description. In this work, a dataset including 1499 samples is collected via SCADA [33,34] for the training of the proposed model. Te voltage profles on the high-voltage (U a , U b , and U c ) and low-voltage (u a , u b , and u c ) sides are presented in Figures 4 and 5, respectively.…”