Mono ANN Module Protection Scheme and Multi ANN Modules for Fault Location Estimation for a Six-Phase Transmission Line Using Discrete Wavelet Transform

Document Type : Research paper

Authors

Department of Electrical Engineering, National Institute of Technology, Warangal, India

Abstract

The enhanced power transfer capability is possible with the six-phase transmission system but it did not gain popularity due to the lack of a proper protection scheme to secure the line from 120 types of different possible short circuit faults. This work presents a protection scheme with discrete wavelet transform (db4 mother wavelet) and an artificial neural network (ANN). The Levenberg-Marquardt algorithm is used for training the ANNs. This protection scheme requires only the pre-processed current information of the sending end bus. For fault detection and classification of all 120 fault types, a single ANN module is implemented with six inputs and six outputs. For fault location estimation in each phase, 11 ANN modules with six outputs are implemented, one for each of the 11 types of combination of faults. The MATLAB/ SIMULINK simulation results of the proposed protection technique implemented on the six-phase Allegheny power transmission system show that it is effective and efficient in detecting and classifying all the faults with varying fault parameters with an accuracy of 99.76%. It is found that the performance of the fault location estimation modules is better with the training data and moderate with the testing data.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 12, Issue 4
December 2024
Pages 337-351

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History

  • Receive Date: 18 October 2022
  • Revise Date: 16 January 2023
  • Accept Date: 22 April 2023

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