Data-driven joint topology and line parameter estimation for renewable integration

Jiafan Yu, Yang Weng, Ram Rajagopal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The increasing integration of distributed energy resources (DERs) calls for new planning and operational tools in the distribution grids to ensure stability and sustainability. The current system planning and operation usually depend on the knowledge of the system model, in particular, the topology and line parameters. However, such system information may be missing or inaccurate in distribution grids, leading to difficulties in calculating DER's locational benefits and planning DER's growth. While the data-driven joint estimation of the topology and the line parameters under noiseless scenario can be achieved by solving a linear system of equations, the problem under noisy scenario is hard. This is because noises appear in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). To solve this problem with accurate modeling, we propose the error-in-variables (EIV) model in a maximum likelihood estimation (MLE) problem. While directly solving the problem is NP-hard, we adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. Interestingly, the new problem has a closed form solution while being non-convex. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from South California Edison. Notably, our parameter estimation is highly accurate even without topology information.

Original languageEnglish (US)
Title of host publication2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
Volume2018-January
ISBN (Electronic)9781538622124
DOIs
StatePublished - Jan 29 2018
Externally publishedYes
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: Jul 16 2017Jul 20 2017

Other

Other2017 IEEE Power and Energy Society General Meeting, PESGM 2017
CountryUnited States
CityChicago
Period7/16/177/20/17

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ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Yu, J., Weng, Y., & Rajagopal, R. (2018). Data-driven joint topology and line parameter estimation for renewable integration. In 2017 IEEE Power and Energy Society General Meeting, PESGM 2017 (Vol. 2018-January, pp. 1-5). IEEE Computer Society. https://doi.org/10.1109/PESGM.2017.8274182