Title :
Ant colony optimization of rough set for HV bushings fault detection
Author :
Mpanza, L.J. ; Marwala, T.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Johannesburg, Johannesburg, South Africa
Abstract :
In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who´s data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an accuracy 4% high than that of EFB rough set. Rules generated are only a third for ACO compared to EFB. Although ACO takes longer to train, it proves to outperform EFB in all other respects.
Keywords :
bushings; data mining; fault diagnosis; mechanical engineering computing; optimisation; rough set theory; HV bushings fault detection; ant colony optimization; data mining; equal frequency bin; oil impregnated paper bushings; rough set method; Accuracy; Approximation methods; Data mining; Fault detection; Insulators; Mathematical model; Optimization;
Conference_Titel :
Advanced Computational Intelligence (IWACI), 2011 Fourth International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-61284-374-2
DOI :
10.1109/IWACI.2011.6159982
