Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/98815
Type: Artigo de evento
Title: Reliable Bad Data Processing For Real-time State Estimation.
Author: Monticelli A.
Garcia A.
Abstract: The weighted least squares performance index test conventionally used in power system static state estimation has poor reliability for detecting the presence of measurement errors in the range 3 to 20 standard deviations. This paper describes a simple alternative method, with improved bad data detection properties, based on evaluating the coherency between the measurement with the largest normalized residual and the remainder of the measurement system. The detection and identification phases of bad data processing are combined. In an existing state estimator using normalized residuals, the cost of implementation of the new method is negligible. Although the new method is simple in application, the underlying estimation theory merits attention. The paper includes a summary of the relevant mathematics.
Editor: IEEE, New York, NY, USA
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0020336785&partnerID=40&md5=6bc351bc40cc83f3d010d6ee94b6c7e0
Date Issue: 1982
Appears in Collections:Unicamp - Artigos e Outros Documentos

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