Data-driven Robust State Estimation through Off-line Learning and On-line Matching

Yanbo Chen, Hao Chen, Yang Jiao, Jin Ma, Yuzhang Lin

Research output: Contribution to journalArticlepeer-review


To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods; then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

Original languageEnglish (US)
Article number9447249
Pages (from-to)897-909
Number of pages13
JournalJournal of Modern Power Systems and Clean Energy
Issue number4
StatePublished - Jul 2021


  • Robust state estimation
  • collinearity
  • historical snapshot
  • offline learning
  • on-line matching

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology


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