An improved Back Propagation (BP) artificial neural network is utilized to assess the insulation condition of a large oil immersed electric power transformer in this paper. After a complete comparison of performances between a few different network architectures, a new kind of BP network structure with a promoted learning algorithm is chosen to train the diagnostic network. Furthermore, some techniques in the reliability analysis of data is introduced into the BP network so as to realize pre-treatment of the data acquired through Dissolved Gas Analysis (DGA), as it is a useful tool for assessing oil-paper insulation. It is verified by the DGA data from substations, that the improved BP algorithm bound with the technique of data pre-treating obtained much higher accuracy. So, it is worthy of being applied for insulation diagnosis in utilities.
[1]
Yukio Mizuno,et al.
Diagnosis of oil-insulated power apparatus by using neural network simulation
,
1997
.
[2]
Sun Guang-wei.
Learning algorithm for feedforward neural networks
,
2001
.
[3]
S. Sitharama Iyengar,et al.
Learning algorithms for feedforward networks based on finite samples
,
1996,
IEEE Trans. Neural Networks.
[4]
P. J. Griffin,et al.
An Artificial Neural Network Approach to Transformer Fault Diagnosis
,
1996,
IEEE Power Engineering Review.
[5]
Kurt Hornik,et al.
Multilayer feedforward networks are universal approximators
,
1989,
Neural Networks.
[6]
Yan Zhang,et al.
FAULT DIAGNOSIS OF INSULATION IN POWER TRANSFORMER BASED ON DISSOLVED GAS ANALYSIS METHOD
,
2000
.