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Detection of Faults on Nigeria 330kv Power Transmission Lines Using Artificial Intelligence (AI)
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The occurrence of faults on Power Transmission Lines is inevitable. Various methods have been applied by researchers to detect these faults. Among the methods used for detecting faults on the transmission lines, Artificial Neural Network (ANN) been one of the recent artificial intelligence methods for fault detection has been employed in this research for the detection of fault on 330kV Power Transmission Line. Simulations were performed using MATLAB/Simulink R2018a on ANN fault detector with the pre-fault and fault signals as inputs of the ANN fault detector in order to identify the various faults that occurred on the line. The results showed that, the best training performance for the detection of faults was achieved at Mean Square Error (MSE) of 1.6856e-5. and at Regression approximately equal to zero (9.8958e-1). The simulation result is satisfactory since it satisfied the standard training performance of any ANN fault diagnosis system.
Keywords
Power System, Transmission Line, Matlab/Simulink, Three Phase, Faults
References
[1] Gupta S. K. (2009) Power System Engineering January 2009’’. 4232/1, Ansari Road, Dariyaganj, Delhi-110002 ISBN: 978-81-88114-91-7
[2] Ogboh, V. C., Nwangugu, E. C., &Anyalebechi, A. E. (2019). Fault Detection on Power System Transmission Line Using Artificial Neural Network (A Comparative Case Study of Onitsha–Awka–Enugu Transmission Line. American Journal of Engineering Research (AJER) 2019, 8(4), 32-57.
[3] Almobasher, L. R., & Habiballah, I. (2020). Review of Power System Faults. International Journal of Engineering Research & Technology (IJERT).
[4] Ogboh V. C, Ezechukwu O. A, Madueme T. C. (2019) Analysis of Fault Detection Algorithm for Power System Transmission Lines Using DFT And FFT’’. Ire Journals | Volume 3 Issue | ISSN: 2456-8880 Ire 1701668 Iconic Research and Engineering Journals 142.
[5] Saritha M., Viji C., Aravind K., Joel O. B., Linu B., Juhina A., Priya S. (2015). Fault Detection and Location in Transmission Line using Pole Climbing Robort’’. International Journal of Engineering Science & Research Technology. ISSN: 2277-9655. March, 2015.
[6] Anazia E. A, Ogboh V. C., Anionovo U. E. (2020). Time – Frequency Analysis Technique for Fault Investigation on Power System Transmission Lines. International Journal of Engineering Inventions, 9(6), 18-25.
[7] Okwudili O. E., Ezechukwu O. A., Onuegbu J. (2019). Artificial Neural Network Method for Fault detection on Transmission Line’’. International Journal of Engineering Inventions E-ISSN: 2278-7461, P-ISSN: 2319-6491 Volume 8, Issue 1 [January 2019] PP: 47-56 www.Ijeijournal.Com Page | 47
How to cite this paper
@article{1704056,
author = {Atuchukwu J. A, Ochogwu S. O., Okonkwo I. I., Ogboh V. C.},
title = {Detection of Faults on Nigeria 330kv Power Transmission Lines Using Artificial Intelligence (AI)},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {7},
pages = {390-404},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704056.pdf},
abstract = {The occurrence of faults on Power Transmission Lines is inevitable. Various methods have been applied by researchers to detect these faults. Among the methods used for detecting faults on the transmission lines, Artificial Neural Network (ANN) been one of the recent artificial intelligence methods for fault detection has been employed in this research for the detection of fault on 330kV Power Transmission Line. Simulations were performed using MATLAB/Simulink R2018a on ANN fault detector with the pre-fault and fault signals as inputs of the ANN fault detector in order to identify the various faults that occurred on the line. The results showed that, the best training performance for the detection of faults was achieved at Mean Square Error (MSE) of 1.6856e-5. and at Regression approximately equal to zero (9.8958e-1). The simulation result is satisfactory since it satisfied the standard training performance of any ANN fault diagnosis system.},
keywords = {Power System, Transmission Line, Matlab/Simulink, Three Phase, Faults},
month = {January},
}