Erroneous measurement detection in substation automation system using OLS based RBF neural network

Su Sheng, Duan Xianzhong, Wai Lok Chan, Li Zhihuan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

With the development of communication and information technology over the past decades, Electronic Instrumental Transducer (EIT) and broadband communication network have been prevalent within Substation Automation System (SAS) and power utilities. Since mal-function of EIT and broadband communication network within SAS can produce dangerous erroneous measurements, the risk for the protection system to receive these erroneous measurements and thereafter to mis-operate increase. Pattern identification can be utilized to detect erroneous measurements. In order to achieve satisfying pattern identification precision within time limit imposed by protection systems, Radial Basis Function Neural Network (RBFNN) are investigated in the paper. Orthogonal Least Square (OLS) learning algorithm is used to prune network scale in order to mitigate contradictory requirements of high precision and low time delay. Simulation results show OLS based RBFNN can achieve satisfying performance within limited time.
Original languageEnglish
Pages (from-to)351-355
Number of pages5
JournalInternational Journal of Electrical Power and Energy Systems
Volume31
Issue number7-8
DOIs
Publication statusPublished - 1 Sep 2009

Keywords

  • Erroneous measurement
  • Orthogonal least square learning algorithm
  • Radial basis function neural networks
  • Substation automation systems

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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