A novel approach for state estimation using generative adversarial network

Yi He, Songjian Chai, Zhao Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Accurate power system state estimation is essential for power system control, optimization, and security analysis. In this work, a model-free approach was proposed for power system static state estimation based on conditional Generative Adversarial Networks (GANs). Comparing with conventional state estimation approach, i.e., Weighted Least Square (WLS), any appropriate knowledge of system model is not required in the proposed method. Without knowing the specific model, the GANs can learn the inherent physics of underlying state variables purely relying on historic samples. Once the model has been well trained, it can generate the corresponding estimated system state given the system raw measurements. Particularly, the raw measurements are sometimes characterized by incompletion and corruption, which gives rise to significant challenges for conventional analytic methods..The case study on IEEE 9-bus system validates the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2248-2253
Number of pages6
ISBN (Electronic)9781728145693
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
Duration: 6 Oct 20199 Oct 2019

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2019-October
ISSN (Print)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Country/TerritoryItaly
CityBari
Period6/10/199/10/19

Keywords

  • Conditional GAN
  • Deep learning
  • Generative adversarial network
  • State estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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