1. Neural Network Application for Fault Analysis
ECEN 679: Computer Relays
Spring 2014
Project 1
Anupam Thatte
UIN:

2. Outline Fault Analysis Pattern Recognition
Artificial Neural Network (ANN)
Learning
Classification
Advantages and Drawbacks of ANN
References

3. Fault Analysis SCADA – Alarm analysis
Digital Fault Recorder (DFR) and Sequence of Events Recorder (SER) file analysis
Restoration strategies
Incipient fault detection on power transformers
Monitoring circuit breaker operation

4. Pattern Recognition
Source: Google Images,

5. Artificial Neural Network (ANN)
There can be different threshold functions, most popular is sigmoid.
Source: Google Images

6. ANN for fault analysis Source: [Dalstein 1995]
ANN will have 1 input and 1 output layer. ANN can have several hidden layers.
Data acquisition is aimed at collecting samples of analog quantities (voltage and currents) from the secondaries of instrument transformers, and status information (contacts) from circuit breakers, switches, and relays.
Source: [Dalstein 1995]

7. Learning Determine weights w1, w2, w3,…
Need large dataset for training
EMTP simulation of different fault types, locations etc. to generate
Training data
Testing data

8. Classification
Source: [Kezunovic Rikalo 1996]

9. Advantages Samples of fault currents and voltages can be directly used
Outcome in terms of classes
Robust with noisy data
Fast to process new input data

10. Drawbacks May need large training data set Training time may be long
Difficult to interpret – “black box”
Design is not straight forward
Number of hidden layers
Choice of threshold function
Learning algorithm etc.
No clear guidance on this in fault analysis literature!
Design of ANN for fault analysis is not clear. In the literature I could not find clear guidance on design.

11. References
M. Kezunovic “A Survey of Neural Net Applications to Protective Relaying and Fault Analysis,” Engineering Intelligent Systems, vol. 5, no. 4, Dec1997.
M. Kezunovic, I. Rikalo, and D. Sobajic, “Real-Time and Off-Line Transmission Line Fault Classification Using Neural Networks”, Intl. Journal of Engineering Intelligent Systems, vol. 4, no. 1, Mar.1996.
S. Haykin, Neural networks: a comprehensive foundation, Prentice Hall, Upper Saddle River, NJ, USA, 1999.
A. K. Jain, J. Mao, and K. M. Mohiuddin, Artificial Neural Networks: A Tutorial, Computer, pp , Mar1996.
K. S. Swarup and H. S. Chandrasekharaiah, "Fault detection and diagnosis of power system using artificial neural networks", Proc. 1st Int. Forum on Applicat. Neural Networks to Power Syst., pp , 1991.
K. R. Niazi, C. M. Arora, and S. L. Surana, Power system security evaluation using ANN: feature selection using divergence, Electric Power Systems Research, vol. 69, issues 2–3, pp , May

12. References
T. Dalstein and B. Kulicke "Neural Network Approach to Fault Classification for High Speed Protective Relaying", IEEE Trans. on Power Delivery, vol. 10, no. 2, pp , 1995.
E. E. Vazquez, H. Altuve, and O. Chacon, "Neural network approach to fault detection in electric power systems", IEEE Trans. Power Delivery, vol. 11, pp , 1996.
A.P. Alves da Silva, A. H. F. Insfran, P. M. Da Silveira, and G. L. Torres, "Neural networks for fault location in substations," IEEE Trans. Power Delivery, vol.11, no.1, pp , Jan 1996.
M. Kezunovic and I. Rikalo, "Detect and classify faults using neural nets," IEEE Computer Applications in Power, vol.9, no. 4, pp , Oct
E. H. P. Chan, "Application of neural-network computing in intelligent alarm processing (power systems)," Power Industry Computer Application Conference (PICA), pp , 1-5 May 1989.
ECEN 679 Reading – J.G. Webster, Wiley Encyclopedia of Electrical and Electronics Engineering, Wiley & Sons, (Chapter: M. Kezunovic and H. Drazenovic-Perunicic, Fault Location)

13. Backup Learning Algorithms Fault Location requirements Types of ANNs
Supervised vs. Unsupervised learning

14. Learning Algorithms
Error-correction learning: e.g. backpropagation algorithm
Memory based learning: e.g. k nearest neighbor
Hebbian learning
Competitive learning
Boltzmann learning: similar to error- correction but considers probability distribution

15. Fault Location Requirements
Determine fault type so proper auto- reclosing option can be applied.
Accurate to within a span of two towers. Typically 0.1% error is acceptable.
Accurate even if only few cycles (3-4) of data is measured.
Accuracy should not deteriorate for various types of faults.

16. Types of ANNs Feed Forward Neural Network: Recurrent Network:
Information moves in only one direction from input through hidden layer to output
No cycles or loops in the network
Recurrent Network:
Include both feed-forward and feed-back
Can do computations on an input of variable length

17. Supervised vs Unsupervised
Supervised Models
Neural Networks
Multi-Layer Perceptron
Decision Trees
Unsupervised Models
Different Types of Clustering
Distances and Normalization
Kmeans
Self Organizing Maps