Fault Location in Distribution Systems based on Artificial Neural Networks and Application of GIS

1. M.Zangiabadi M.R.Haghifam A.Khanbanha University of Tehran Tarbiat Modares University Kerman Regional Electric Co. Fault Location in Distribution Systems based on Artificial Neural Networks and Application of GIS

2. Fault Location Estimation • Off-Line Methods • Trial and error method with energization the line section by section • On-Line Methods • High frequency transient signals • Wavelets • Pattern recognition • Neural network

3. Case Study • Input data for neural network • Voltage • Current • Simulation software • EMTDC • MATLAB • Line model • Bergeron model configuration of feeder and simulator output

4. Simulator Output Single-phase to ground fault in the middle of feeder Three-phase to ground fault in the middle of feeder

5. The Proposed Neural Network Structure • Three-layer feed forward neural network • Error back propagation training method • Input data – voltage and current – are normalized • Output layer • Distance • Flag which refers to lateral number

6. Fault Location (m) Fault Resistance (Ω) Error of ANN predicted location (m) Lateral Number 400 5 5.227 0 1000 10 7.439 0 1300 15 9.247 0 1500 5 2.09 0 1800 10 0.9 0 2100 15 3.32 0 2400 10 3.53 0 2800 5 1.607 0 The results of L-G fault • Training data is prepared in pitches of 50 meters • The resistance of fault is changed in steps of 5 from 0 to 25 ohms

7. Selecting the best structure • Number of epochs is considered 1000 epochs • Mean Square Error criterion evaluates the structure Error Percentage of Neural Network for L-G fault

8. Structure of Neural Networks as Fault Locator Distance Distance

9. Combination with GIS Software

10. Thanks for your attention I would also like to thank University of Tehran (UT) and Kerman Regional Electric Company (KREC) for their supports