Neural-Fuzzy Pattern Recognition Algorithm for Classifying the Events in Power System Networks

1. Neural-Fuzzy Pattern Recognition Algorithm for Classifying the Events in Power System Networks Slavko Vasilic Department of Electrical Engineering Texas A&M University

2. Outline • Problem, Goal, Objectives • Protective relaying • Neural network (NN) algorithm • Process modeling and simulation • Algorithm implementation • Fuzzyfication of NN outputs • Algorithm Testing • Conclusion, Future Work

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 • Demonstrate the benefits using realistic network and fault events

5. Objectives • 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. Protective Relaying The different parts of the fault clearance chain

7. Protective Relaying The principle of distance protection relays

8. Protective Relaying Mho fault characteristic of distance relay

9. Neural Network Algorithm The principle of multilayer neural networks

10. Neural Network Algorithm Pattern classification of faulted events Class decision boundaries Patterns

11. Neural Network Algorithm Characteristic of the used neural network • Direct use of samples (no feature extraction) • Hidden layer of competitive neurons • Self-organizing • Unsupervised and supervised learning • Outputs are prototypes of typical patterns • Adaptability for non-stationary inputs

12. Neural Network Algorithm Training steps

13. Process Modeling and Simulation RE HL&P Stp-Sky power network model

14. Process Modeling and Simulation Scenario cases: general fault events • All types of fault (11 types) • Fault location variation (0-100% of the line length) • Fault impedance variation (0-100 Ohms) • Fault inception angle variation (0-360 deg)

15. Process Modeling and Simulation Example of patterns for various fault parameters

16. 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 forming the inputs for algorithm training and evaluation

17. Algorithm Implementation Training and testing (cont’d) • Training tasks are aimed at recognizing fault type and location • Test patterns correspond to a new set of previously unseen scenarios • Test patterns are classified according to their similarity to the prototypes by applying K-nearest neighbor classifier (decision rule)

18. Algorithm Implementation Properties of input 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

19. Algorithm Implementation Moving data window for taking the samples

20. Algorithm Implementation Example of the patterns for various scaling ratios

21. Prototype Algorithm Implementation Training patterns

22. Algorithm Implementation The outcome of training are pattern prototypes

23. Fuzzyfication of NN Outputs Fuzzyfied classification of a test pattern

24. Fuzzyfication 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

25. Fuzzyfiaction of NN Outputs

26. Fuzzyfication of NN Outputs Fuzzy K-NN parameter optimization

27. Test pattern Algorithm Testing Nearest prototypes

28. Algorithm Testing Propagation of classif. error during testing

29. Algorithm Testing Algorithm sensitivity versus data used for training

30. 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

31. 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

32. Future Work • Perform comprehensive algorithm training for extended set of training patterns • Perform extensive algorithm testing and performance optimization • Study algorithm sensitivity versus various input signal preprocessing steps • Implement algorithm on-line learning