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Identification of Defective Insulators in Power Lines using Neural Networks

The project focuses on identifying and locating defective insulator strings in power lines through artificial neural networks. It utilizes current transformers as capacitive couplers for signal acquisition, allowing remote monitoring of insulators. The research includes corona effect emulation and software development for pattern recognition, with a detailed test on locating damaged insulator strings. The integration of neural networks proves to be a powerful and precise tool in the identification process, ultimately leading to cost savings.

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Identification of Defective Insulators in Power Lines using Neural Networks

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  1. TheUse of Artificial Neural Networks for Identification and Location of Defective Insulators in Power Lines Through Current Transformers Renato Massoni Capelini – UNIFEI – Brazil Manuel Luiz Barreira Martinez – UNIFEI - Brazil José Feliciano Adami – UNESP – Brazil Marcel Fernando da Costa Parentoni – UNIFEI - Brazil Ithamar Sene Junior – AES Sul - Brazil CAPELINI– BR – RIFS3– 0572

  2. Purpose of the Project • Remote identification and location of defective insulator strings • Use the current transformer as a capacitive coupler for signal acquisition CAPELINI– BR – RIFS3– 0572

  3. Defective Insulator Strings CAPELINI– BR – RIFS3– 0572

  4. Corona Effect • Partial Discharge • Low Amplitude • High Frequency CAPELINI– BR – RIFS3– 0572

  5. Corona Effect Emulation CAPELINI– BR – RIFS3– 0572

  6. The Laboratory 48 km Artificial Transmission Line 48 Taps in the Transmission Line (4 taps/pi) CAPELINI– BR – RIFS3– 0572

  7. The Laboratory CAPELINI– BR – RIFS3– 0572

  8. CT as a Coupling Device CAPELINI– BR – RIFS3– 0572

  9. CT as a Coupling Device CAPELINI– BR – RIFS3– 0572

  10. AI: Neural Networks • Based on the Human Brain • Good for Pattern Recognition CAPELINI– BR – RIFS3– 0572

  11. Software Development CAPELINI– BR – RIFS3– 0572

  12. Software Development Test 1 • Location of the Damaged String in the Power Line • String: 1 Defective Insulator • 12 Taps of the Power Line (Step: 4 km) • Data Bank: 12000 Signals from the Damaged String CAPELINI– BR – RIFS3– 0572

  13. Software Development Test 1 Tap 1 Tap 5 Tap 9 Tap 13 Tap 17 Tap 45 CAPELINI– BR – RIFS3– 0572

  14. Software Development Test 1 CAPELINI– BR – RIFS3– 0572

  15. Conclusion • CT as a Coupling Device → $ $ $ Saving • Neural Network → Very Powerful and Precise Tool • Data Bank Size → Direct Influence on the Results CAPELINI– BR – RIFS3– 0572

  16. Thank You! CAPELINI– BR – RIFS3– 0572

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