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

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

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

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

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

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

Identifying Context Using NN Training steps

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

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

Algorithm Implementation Example of patterns for various fault parameters

Algorithm Implementation The outcome of training are pattern prototypes

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

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

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

Advanced Processing of NN Outputs Fuzzyfied classification of a test pattern

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

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

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

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

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