1) Artificial neural networks can be used for fault diagnosis of power transformers based on dissolved gas analysis. Different ANN models are trained to classify fault types based on inputs of different dissolved gas ratios from techniques like Rogers ratio and Duval triangle. 2) A smart fault diagnostic approach uses the outputs of each ANN model to make a normalized decision on fault type. This includes classifications like no fault, thermal fault, arcing, partial discharge, and undetermined fault. 3) The smart fault diagnostic approach can be enhanced by adding another ANN that is trained directly on raw gas concentration data, and integrating its output with the existing approach to improve accuracy of fault classification.

1. ANN BASED FAULT DIAGNOSTIC
SCHEME OF
POWER TRANSFORMER
PRESENTED BY :
Mohammad Sohaib
Mohd. Saif Rayeen
Mohammad Ateeb Masood

2. ANN-Artificial Neural Networking
• Mathematical model inspired by the structural and functional aspects of
biological neural networks.
• Feed forward & Recurrent types.
• Feed forward ANN with back propagation (ability to recognize pattern).

3. Implementation of ANN in DGA
• Input  Dissolved gas ratio ( each method respectively )
• Output Fault type (for each diagnostic method )
• Two transition hidden layers of neuron ( sigmoid functions )
• Rogers ( input 3 , output 7 )
• Dornenburg ( input 4 , output 5)
• CEGB ( input 4, output 13 )
• IEC ( input 3 , output 10 )
• Duval ( input 3 , output 10 )

4. Smart Fault Diagnostic Approach
(SFDA)
• Based on the O/P of each method’s neural network.
• Using the decision making concept fault is identified.
• Each neural network’s O/P is normalized to be on of the five decisions
- NF ( no fault )
- TH ( thermal fault )
- AR ( arching )
- PD ( partial discharge )
-UD ( undetermined fault )

5. Flowchart of SFDA [1]

6. Evaluation of SFDA
Table 1. Agreement percentages of the respective methods [1]
Table 2. Agreement percentages on integrating three methods [1]

7. Enhancement of SFDA
• SFDA is enhanced by addition of a new ANN.
• The added concept is called CSUS.
• Raw data of the gas concentrations are used to train a new ANN
• This neural network is then integrated with the SFDA result to get more
accurate result.

8. Comparison of CSUS with the existing result
Table 3 [1]

9. References
• [1] S. S. M. Ghoneim et al.: Integrated ANN-
Based Proactive Fault Diagnostic Scheme for
Power Tranformer 2016 .