TY - GEN
T1 - Artificial neural network and rough set for HV bushings condition monitoring
AU - Mpanza, L. J.
AU - Marwala, T.
PY - 2011
Y1 - 2011
N2 - Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.
AB - Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.
UR - http://www.scopus.com/inward/record.url?scp=80051729493&partnerID=8YFLogxK
U2 - 10.1109/INES.2011.5954729
DO - 10.1109/INES.2011.5954729
M3 - Conference contribution
AN - SCOPUS:80051729493
SN - 9781424489565
T3 - INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings
SP - 109
EP - 113
BT - INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings
T2 - 15th International Conference on Intelligent Engineering Systems, INES 2011
Y2 - 23 June 2011 through 25 June 2011
ER -