Abdullahi Abubakar Mas'ud
Statistical error tolerances of partial discharge recognition rates.
Mas'ud, Abdullahi Abubakar; Eltayeb, Mohammed E.; Muhammad-Sukki, Firdaus; Bani, Nurul Aini
Mohammed E. Eltayeb
Nurul Aini Bani
This paper compares the statistical error tolerances of the single neural network (SNN) and the ensemble neural network (ENN) recognition efficiencies, when both the SNN and ENN are applied to recognize partial discharge (PD) patterns. Statistical fingerprints from the phased and amplitude resolved patterns of PDs, have been applied for training and testing the SNN and the ENN. Statistical mean and variances of the SNN and ENN recognition rates were compared and evaluated over several iterations in order to obtain an acceptable value. The results show that the ENN is generally more robust and often provides an improved recognition rate with higher mean value and lower variance when compared with the SNN. The result implies that it is possible to determine the accurate statistical error tolerances for the SNN and ENN recognition probability for correct diagnosis of PD fault.
|Start Date||Oct 15, 2015|
|Publication Date||Oct 15, 2015|
|Publisher||Institute of Electrical and Electronics Engineers|
|Institution Citation||MAS'UD, A.A., ELTAYEB, M., MUHAMMAD-SUKKI, F. and BANI, N.A. 2015. Statistical error tolerances of partial discharge recognition rates. In Proceedings of the Institute of Electrical and Electonic Engineers (IEEE) conference on sustainable utilization and development in engineering and technology (CSUDET 2015), 15-17 October 2015, Selangor, Malaysia. Piscataway: IEEE [online], article ID 7446217. Available from: https://dx.doi.org/10.1109/CSUDET.2015.7446217|
|Keywords||Ensemble neural network; Partial discharge; Single neural network|
MAS'UD 2015 Statistical error tolerances of partial