ClasLoc : A Robust Fault Management Methodology for Transmission Lines Using RBFNN and ANFIS

Authors

  • Kazi Md Shahiduzzaman Associate Professor, Department of Electrical and Electronics Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh Author
  • Alpona Akter Koly Department of Electrical and Electronics Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh Author
  • Mahbuba Maria Department of Electrical and Electronics Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh Author

DOI:

https://doi.org/10.32628/CSEIT24104127

Keywords:

Transmission Line Fault, RBFNN, ANFIS, Fault Management

Abstract

Transmission lines are crucial for efficient and reliable electricity delivery of electricity but can also be susceptible to faults due to various factors such as external interference, device failures, and ambient conditions. The reliability, accuracy, and efficiency of fault management on the power grid depend heavily on fault classification and fault location. This study integrates a fault detection and classification system for transmission lines and an automatic fault localisation system (ANFIS) for fault location to present a comprehensive approach to managing transmission line problems. The proposed methodology aims to address the technological challenges associated with fault management in power grids by leveraging existing research on radial basis function neural networks (RBFNN) and ANFIS. The integration and validation of the proposed methodology involve incorporating improved fault classification via RBFNN and robust fault locating through ANIS. Simulations and real-world testing will validate the integrated methodology, assessing its performance in various fault scenarios and system configurations. The WSCC 9-bus system is used to validate and test the proposed design, which includes power plants, transformers, transmission networks, and distribution networks. The ClasLoc system has a classification accuracy of 90% and a location accuracy of 89%, ensuring a safe working environment.

Downloads

Download data is not yet available.

References

A.I. Nuno, B. Arcay, J. M. Cotos and J. A. Varela, "Optimisation of fishing predictions by means of artificial neural networks, anfis, functional networks, and remote sensing images".

A. S. Neethu and T. S. Angel. "Smart fault location and fault classification in transmission line". 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy, and Materials (ICSTM). pp. 339-343. Oct. 2017. 10.1109/ICSTM.2017.8089181. DOI: https://doi.org/10.1109/ICSTM.2017.8089181

A. Mukherjee, P. K. Kundu, and A. Das. "Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods". Journal of Institution of Engineers (India) series B. Jan. 2021. 10.1007/s40031-020-00530-0. DOI: https://doi.org/10.1007/s40031-020-00530-0

Z. Moravej, D. N. Vishwakarma, and S. P. Singh. "Application of radial basis function neural network for differential relaying of a power transformer".

Computers & Electrical Engineering. Vol. 29. no. 3. pp. 421-434. May 2003. https://doi.org/10.1016/S0045-7906(01)00033-7. DOI: https://doi.org/10.1016/S0045-7906(01)00033-7

G. K. Sun, J. X. Yu and M. Yuan. "For Transmission Line Fault Type Recognition Based on RBF Neural Network". Applied mechanics and materials. vol. 687-691. pp. 895-899. Nov. 2014. 10.4028/www.scientific.net/amm.687-691.895. DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.895

Y. Hou et al. "The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis".

H. Duan and X. Yao. "Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network". Advanced Materials Research. vol. 614-615. pp. 1303-1306. Dec. 2012. 10.4028/www.scientific.net/amr.614-615.1303. DOI: https://doi.org/10.4028/www.scientific.net/AMR.614-615.1303

L. Tang and D. Xiang. "A Travelling Wave Differential Protection Scheme for Half-Wavelength Transmission Line". Elsevier BV. vol. 99. pp. 376-384. Jul. 2018. 10.1016/j.ijepes.2018.01.018. DOI: https://doi.org/10.1016/j.ijepes.2018.01.018

A. N. Kumar, P. Sridhar, T. A. Kumar, T. R. Babu, and V. Mohan. "Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines". International Journal of Artificial Intelligence (IJ-AI). vol. 9. no. 3. pp. 8. September 2020. 10.11591/ijai.v9.i3.pp448-455. DOI: https://doi.org/10.11591/ijai.v9.i3.pp448-455

R. N. Mahanty and P. Gupta. "Application of RBF neural network to fault classification and location in transmission lines". Institution of Engineering and Technology. vol. 151. no. 2. pp. 201-201. Jan. 2004. 10.1049/ip-gtd:20040098. DOI: https://doi.org/10.1049/ip-gtd:20040098

K. Narendra, V. K. Sood, K. Khorasani, and R. V. Patel. "Application of a Radial Basis Function (RBF) Neural Network for Fault Diagnosed in an HVDC System". Institute of Electrical and Electronics Engineers. vol. 13. No. 1. pp. 177-183. Jan. 1998. 10.1109/59.651633. DOI: https://doi.org/10.1109/59.651633

L. Mejdi, F. Kardous, and K. Grayaa, "Experimental Validation of PV Power Prediction with ML Models for Improved Grid Integration," 2023 20th International Multi-Conference on Systems, Signals & Devices (SSD), Mahdia, Tunisia, 2023, pp. 439-445, doi: 10.1109/SSD58187.2023.10411278. DOI: https://doi.org/10.1109/SSD58187.2023.10411278

Kazi Md Shahiduzzaman, Md Noor Jamal, & Md. Rashed Ibn Nawab, (2021). Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis. International Journal of Engineering and Advanced Technology (IJEAT), 10(6), 11–18. https://doi.org/10.35940/ijeat.E2689.08 10621. DOI: https://doi.org/10.35940/ijeat.E2689.0810621

A. Y. Hatata et al. (2016) 'Transmission Line Protection Scheme for Fault Detection, Classification, and Location Using ANN' International Journal of Modern Engineering Research (IJMER) | Vol. 6 | Iss. 8 | August 2016 | 1 |.

TAWAFAN, Adnan; SULAIMAN, Marizan Bin; IBRAHIM, Zulkifilie Bin. "Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High-Impedance Fault on Distribution Power System". International Journal of Artificial Intelligence (IJ-AI). Vol. 1. no. 2. pp. 63-72. Jun. 2012. http://dx.doi.org/10.11591/ij-ai.v1i2.425. DOI: https://doi.org/10.11591/ij-ai.v1i2.425

M. Jamil, S. K. Sharma, and R. Singh. "Fault detection and classification in electrical power transmission system using an artificial neural network". Springer International Publishing. vol. 4. no. 1. July 2015. 10.1186/s40064-015-1080-x. DOI: https://doi.org/10.1186/s40064-015-1080-x

P. Ray, P. M. Debani, K. Dey, and P. Mishra. "Fault Detection and Classification of a Transmission Line Using Discrete Wavelet Transform & Artificial Neural Network". 2017 International Conference on Information Technology (ICIT). Dec. 2017. 10.1109/icit.2017.24. DOI: https://doi.org/10.1109/ICIT.2017.24

P. Yang, T. Wang, H. Yang, C. Meng, H. Zhang, and L. Cheng. The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network". Electronics (MDPI). Vol. 12. No. 4. pp. 1066-1066. Feb. 2023. 10.3390/electronics1204106 6. DOI: https://doi.org/10.3390/electronics12041066

Downloads

Published

15-08-2024

Issue

Section

Research Articles

How to Cite

[1]
Kazi Md Shahiduzzaman, Alpona Akter Koly, and Mahbuba Maria, “ClasLoc : A Robust Fault Management Methodology for Transmission Lines Using RBFNN and ANFIS”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 4, pp. 267–274, Aug. 2024, doi: 10.32628/CSEIT24104127.

Similar Articles

1-10 of 132

You may also start an advanced similarity search for this article.