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A Robust Dynamic Line Rating Monitoring System through State Estimation and Bad Data Analysis

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This paper introduces an approach to increase robustness and safety of a weather and tension-based dynamic line rating system through the use of state estimation and bad data analysis algorithms. Bad data are identified through a procedure based on the evaluation of normalized Lagrange multipliers and collinearity tests. Numerical results highlight the effectiveness of the state estimation and gross error verification algorithms as well as the impact of bad data on dynamic line rating assessments.

Author(s):

Samir Walker Fernandes    
Universidade Federal de Santa Catarina
Brazil

Mauro Augusto da Rosa    
Universidade Federal de Santa Catarina
Brazil

Diego Issicaba    
Universidade Federal de Santa Catarina
Brazil

 

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