Transmission and Distribution Conference and Exposition: Latin America - IEEE/PES 2010, São Paulo (Brasil). 08-10 Noviembre 2010
Resumen:
This paper presents an alternative to the traditional impedance based fault location methods, using a simple technique of the learning approaches called k-Nearest Neighbors (k-NN), where besides the fault location distance, the multiple estimation problem is also addressed. This approach only uses the single end measurements of voltage and current available at the power substation. As principal advantage, considering the classical approaches, this alternative has not dependency on the power system model and also considers the spacial characteristics of the distribution systems. Furthermore, the multiple estimation problem, typical of all fault location approaches, is addressed. According to the proposed tests, faults location in different nodes and values of fault resistances are successfully determined, having an average error rate lower than 1.5% and 13% in distance estimation and zone identification respectively.
Palabras clave: Classification, fault location, k-Nearest Neighbors, learning approaches, multiple estimation, power distribution systems, regression
DOI: 
10.1109/TDC-LA.2010.5762978
Fecha de publicación: noviembre 2010.
Cita:
Morales-España, G., Mora-Flórez, J., Carrillo Caicedo, G., A complete fault location formulation for distribution systems using the k-nearest neighbors for regression and classification, Transmission and Distribution Conference and Exposition: Latin America - IEEE/PES 2010, São Paulo (Brasil). 08-10 Noviembre 2010.
IIT-11-081A
