PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids

TLDR: In this article, an error-in-variables model in a maximum-likelihood estimation framework for joint line parameter and topology estimation is proposed. But the model is not suitable for mesh networks.

Abstract: The increasing integration of distributed energy resources calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using nonzero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables model in a maximum-likelihood estimation framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization algorithm in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from Southern California Edison.