A Data-Driven Method Embedded with Topological Information for Voltage-Power Sensitivity Estimation in Distribution Network

Abstract

The multicollinearity of measurement data leads to the low accuracy of the data-driven methods for estimating voltage-power sensitivity in distribution networks. In this paper, a data-driven method embedded with topological information is proposed to address the problem. First, the voltage-power sensitivity matrix is decomposed into principal and secondary components, where the principal component is closely related to the distribution network topology and the secondary component is the error between the principal component and the actual value. Then, the principal and secondary components are estimated sequentially in two stages, and their data-driven estimation models based on quadratic programming are established, respectively. The key of the model in the first stage is the constraint based on the distribution network topology information, and the key of the model in the second stage is the constraint that the ratio of the secondary component to the principal component is tiny. Finally, the accuracy and efficiency of the proposed method is validated in the IEEE 33-bus system with a set of measurement data, and comparisons are made with ordinary least square regression, ridge regression, and LASSO regression. The simulation results show that the accuracy of the proposed method is significantly improved by orders of magnitude.

Keywords

• voltage-power sensitivity
• distribution network topology
• multicollinearity
• data-driven
• principal component