Mapping Rule Estimation for Power Flow Analysis in Distribution Grids

TL;DR

This paper introduces a support vector regression approach to estimate power flow rules in distribution grids, overcoming challenges of incomplete data and active controllers, thus enhancing grid monitoring and analysis.

Contribution

The paper presents a novel SVR-based method for mapping rule estimation in distribution grids, addressing limitations of traditional regression techniques under real-world conditions.

Findings

SVR outperforms traditional regression in accuracy and robustness.

The method handles measurement outliers and missing data effectively.

Numerical validation confirms the method's effectiveness across various grid scales.

Abstract

The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which may be unavailable in primary and secondary distribution grids. Furthermore, a utility usually has limited modeling ability of active controllers for solar panels as they may belong to a third party like residential customers. To solve the modeling problem in traditional power flow analysis, we propose a support vector regression (SVR) approach to reveal the mapping rules between different variables and recover useful variables based on physical understanding and data mining. We illustrate the advantages of using the SVR model over traditional regression method which finds line parameters in distribution grids. Specifically, the SVR model is robust enough to recover the mapping rules while the regression method fails when 1) there are measurement outliers and missing data, 2) there are active controllers, or 3) measurements are only available at some part of a distribution grid. We demonstrate the superior performance of our method through extensive numerical validation on different scales of distribution grids.