Dependable parallel multi-population modified brain storm optimization for distribution state estimation considering outliers using just-in-time modeling and correntropy

Daichi Azuma, Yoshikazu Fukuyama, Akihiro Oi, Toru Jintsugawa, Hisashi Fujimoto

Research output: Contribution to journalArticle

Abstract

This paper proposes dependable parallel multi-population modified brain storm optimization for load adjustment distribution state estimation (DSE) considering outliers using just in time (JIT) modeling and correntropy. If the outliers are measured at the measurement points, estimation results at the measurement points only using correntropy are affected by the outliers. The challenge is solved by JIT modeling in the proposed method. Evolutionary computation techniques have been applied to the DSE problem because practical equipment causes a nonlinear characteristic of the objective function in distribution systems. However, it is required to obtain high and stable estimation quality in a short time considering uncertain outputs of renewable energies. The challenge is solved by multi-population, and parallel and distributed computing. Moreover, it is also required to keep appropriate estimation quality using dependable calculation even if some of the calculation results of distributed processes cannot be obtained due to various faults of the processes. The proposed method is verified to estimate distribution system conditions more accurately regardless of the outliers, realize faster computation, obtain higher and stabler estimation results, and keep higher quality of solutions even with high fault probabilities than the conventional methods.

Original languageEnglish
Pages (from-to)68-77
Number of pages10
JournalIEEJ Transactions on Power and Energy
Volume140
Issue number2
DOIs
Publication statusPublished - 1 Jan 2020

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Keywords

  • Correntropy
  • Dependability
  • Distribution system
  • Just in time modeling
  • Load adjustment distribution state estimation
  • Parallel multi-population modified brain storm optimization

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