NeuralFuzzy Pattern Recognition Algorithm for Classifying the Events in Power System Networks

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

• Slavko Vasilic
• Department of Electrical Engineering
• Texas AM University

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• 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

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• 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

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• 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

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• 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

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Protective Relaying The different parts of the
fault clearance chain
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Protective Relaying The principle of distance
protection relays
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Protective Relaying Mho fault characteristic of
distance relay
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Neural Network Algorithm The principle of
multilayer neural networks
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Neural Network Algorithm Pattern classification
of faulted events
Class decision boundaries
Patterns
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• 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

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Neural Network Algorithm Training steps
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Process Modeling and Simulation RE HLP Stp-Sky
power network model
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• 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)

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Process Modeling and Simulation Example of
patterns for various fault parameters
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• 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

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• Algorithm Implementation
• Training and testing (contd)
• 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)

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• 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

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Algorithm Implementation Moving data window for
taking the samples
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Algorithm Implementation Example of the patterns
for various scaling ratios
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• Algorithm Implementation

Prototype
Training patterns
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Algorithm Implementation The outcome of training
are pattern prototypes
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Fuzzyfication of NN Outputs Fuzzyfied
classification of a test pattern
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• 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

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• Fuzzyfiaction of NN Outputs

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Fuzzyfication of NN Outputs Fuzzy K-NN parameter
optimization
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• Algorithm Testing

Test pattern
Nearest prototypes
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Algorithm Testing Propagation of classif. error
during testing
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Algorithm Testing Algorithm sensitivity versus
data used for training
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• 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

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• 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

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• 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