Vulnerability of Largest Normalized Residual Test and b̂-Test to Gross Errors

Massignan, Julio A. D.; London, João Bosco Augusto; Vieira, Camila S.; Miranda, Vladimiro

doi:10.1109/pesgm41954.2020.9281995

Cited by 2 publications

(1 citation statement)

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“…Different methods can further aid the estimation, such as employing the largest normalized residue test to detect and identify gross errors Gómez-Expósito, 2004;Massignan et al, 2020). In this context, this final test illustrates the effect of bad data in the normalized residual analysis in each sampling layer.…”

Section: Influence Of Bad Datamentioning

confidence: 99%

A Bayesian perspective for distribution system state estimation: theoretical and practical considerations

Massignan¹

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This Thesis is dedicated to my supportive and loving parents, Eduardo and Mara, my dear brothers João Paulo, Luiz Fernando and Eduardo Junior an to my beloved wife Geise.Thanks to my beloved wife Geise, whose affection, support, patience and trust were my safe harbour in all these years, and with whom I am very happy to share special moments. Thanks also to Ivonete, her mother, Joyce, her sister, a second family that I felt welcomed in.Thanks to Prof. João Bosco Augusto London Jr. that provided me many opportunities and helped me to succeed, for his exceptional support and guidance throughout my career, for providing the resources and space for my research, for his friendship and many councils all these years, and for being the caring person he is, someone I can call a true mentor in my life.Thanks to Prof. Vladimiro Miranda that expanded my research horizon, reinforced my confidence, for the many histories and wisdom shared, and for his true enthusiasm for science.Thanks to Prof. Carlos Maciel and Prof. Benvindo Pereira for the many discussions, exploring new concepts, and for their collaborative support and friendship in all these years.

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Section: Influence Of Bad Datamentioning

confidence: 99%