Title :
Power Transformer Fault Diagnosis using Som-Based RBF Neural Networks
Author :
Liang, Yong-Chun ; Liu, Jian-ye
Author_Institution :
Dept. of Electr. Eng., Hebei Univ. of Sci. & Technol., Shijiazhuang
Abstract :
A radial basis function (RBF) neural network used in fault diagnosis system is developed for power transformer fault analysis. The Gas extracted from transformer oil is the input of RBF-type neural network architecture. Our proposed cell-splitting grid algorithm determines the optimal network architecture of the RBF network automatically. This facilitates the conventional laborious trail-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with the overheating faults and discharging faults of power transformer
Keywords :
fault location; power engineering computing; power transformers; radial basis function networks; self-organising feature maps; SOM-based RBF neural networks; cell-splitting grid algorithm; fault diagnosis system; optimal network architecture; power transformer; radial basis function; Electronic mail; Fault detection; Fault diagnosis; Machine learning; Neural networks; Neurons; Oil insulation; Power system faults; Power system reliability; Power transformers; Radial basis function networks; Testing; Cell-splitting grid (CSG); neural network; radial basis function (RBF); self-organizing map (SOM);
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258406
