1. An Improved Neural Network Algorithm for Classifying the Transmission Line Faults
Slavko Vasilic
Dr Mladen Kezunovic
Texas A&M University

2. Outline Problem Goal Task
Identifying context using neural network (NN)
Algorithm implementation
Advanced processing of NN inputs/outputs
Conclusion

3. Problem
Traditional relay settings are computed ahead of time based on worst case fault conditions and related phasors
The settings may be incorrect for the unfolding events
The actual transients may cause a measurement error that can cause a significant impact on the phasor estimates

4. Goal
Design a new relaying strategy that does not have traditional relay setting
Optimize the algorithm performance in each prevailing network conditions
Improve simultaneously both, dependability and security of the relay operation

5. Task Implement a new pattern recognition based protection algorithm
Use a neural network and apply it directly to the samples of voltage and current signals
Produce the fault type and zone classification in real time
Study various approaches for preprocessing NN inputs and fuzzyfication of NN outputs

6. Identifying Context Using NN Characteristic of the neural network
Direct use of samples (no feature extraction)
Flat structure (no hidden layers)
Self-organizing
Unsupervised and supervised learning
Outputs are prototypes of typical patterns
Adaptability for non-stationary inputs

7. Identifying Context Using NN
Training steps

8. Algorithm Implementation
Training and testing
Power network model is used to simulate various fault events
Fault events are determined with varying fault parameters: type, location, impedance and inception time
The simulation results are used for building the patterns for protection algorithm evaluation

9. Algorithm Implementation Simulation of scenario cases
Training tasks are recognizing the type and zone of the fault
Test patterns correspond to a new set of previously unseen scenarios
Test patterns are classified according to their similarity to established prototypes by applying nearest neighbor classifier

10. Algorithm Implementation
Example of patterns for various fault parameters

11. Algorithm Implementation
The outcome of training are pattern prototypes

12. Advanced Processing of NN Inputs Properties of signal processing
Data selected for training: currents, voltages or both
Sampling frequency
Moving data window length
Analog filter characteristics
Scaling ratio between voltage and current samples

13. Advanced Processing of NN Inputs
Moving data window for taking the samples

14. Advanced Processing of NN Inputs
Example of the patterns for various scaling ratios

15. Advanced Processing of NN Outputs
Fuzzyfied classification of a test pattern

16. Advanced Processing of NN Outputs
Fuzzyfied classification of a test pattern
Determine appropriate number of nearest prototypes to be taken into account
Include the weighted distances between a pattern and selected prototypes
Include the size of selected prototypes

17. Advanced Processing of NN Outputs Fuzzy K-nearest neighbor classifier
prototype fuzzy class membership
test pattern
prototype
considered number of neighboring prototypes
fuzzy weight
test pattern class membership
weighted distance

18. Propagation of classif. error during testing
Algorithm Evaluation
Propagation of classif. error during testing

19. Conclusion
Protection algorithm is based on unique self­organized neural network and uses voltages and currents as inputs
Tuning of input signal preprocessing steps significantly affects algorithm behavior during training and testing
Fuzzyfication of NN outputs improves algorithm selectivity for previously unseen events

20. Conclusion
The algorithm establishes prototypes of typical patterns (events)
Proposed approach enables accurate fault type and fault location classification
The power network model is used to simulate a variety of fault and normal events