AN EXTENDED ANN-BASED HIGH SPEED ACCURATE TRANSMISSION LINE FAULT LOCATION FOR DOUBLE PHASE TOEARTH FAULT ON NON-DIRECT-GROUND

  • Okwudili E. Obi Department of Electrical Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • Oseloka A. Ezechukwu Department of Electrical Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • Chukwuedozie N. Ezema Department of Electronic and Computer Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Keywords: Neural Network, Fault Diagnosis, Learning Strategies, Data Pre-Processing, Double Line - Ground Faults

Abstract

This research has developed an extended Artificial Neural Networks (ANN) based high speed accurate transmission line fault location for double phase to- earth fault on non-direct-ground. Therefore, this research presents a system that capable of detecting and locating the fault with less proportion of error. This system uses the Global Positioning System (GPS) to locate the position and the Global System for Mobile Communication (GSM) to send these messages to system supervisor. A reduction in the size of the neural network improves the performance of the same and this can be achieved by performing feature extraction. By doing this, all of the important and relevant information present in the waveforms of the voltage and current signals can be used effectively. Voltage and current waveforms have been generated and were sampled at a frequency of 720 Hertz. The neural network diagnostic system trained for double faults was found to be able to accurately diagnose abnormal behavior resulting from simultaneous multiple faults. Graceful degradation of the diagnostic system was observed in situations where faults where not accurately diagnosed or under damage to a few nodes.

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Published
2017-01-01
How to Cite
Obi, O. E., Ezechukwu, O. A., & Ezema, C. N. (2017). AN EXTENDED ANN-BASED HIGH SPEED ACCURATE TRANSMISSION LINE FAULT LOCATION FOR DOUBLE PHASE TOEARTH FAULT ON NON-DIRECT-GROUND. International Journal of Engineering Science Technologies, 1(1), 31-47. https://doi.org/10.29121/IJOEST.v1.i1.2017.04