Open Access Journal

ISSN : 2395-2717 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Electrical and Electronic Engineering(IJEREEE)

Monthly Journal for Electrical and Electronic Engineering

ISSN : 2395-2717 (Online)

Call For Paper : Vol 12, Issue 03, March 2025

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Fault Section Estimation by Neural Networks and Genetic Algorithm

Author : Pranay V. Ambade 1 Sanjeev S. Gokhale 2

PDF

Date of Publication :28th March 2018

Abstract: Fault section estimation plays a significant role in the process of restoring the power system to its normal state in minimum time. In this paper, an approach involving artificial neural networks and the genetic algorithm has been used for performing fault section estimation. We have presented a procedure to formulate objective function using neural networks and continuous genetic algorithm. This objective function is then minimized with the help of continuous genetic algorithm and fault section is identified. To validate the efficient performance of the approach, different systems were used for testing the method and it provides accurate results in all cases. One illustration is described in detail. It is seen that solution can be found out effectively in the situation of multiple faults and malfunctioning of protective devices

Reference :

    1. Y. G. Paithankar, and S. R. Bhide, “Fundamentals of power system protection”, Prentice-Hall of India Pvt. Ltd., New Delhi, 2007.
    2. Lai LL. Intelligent system applications in power engineering – evolutionary programming and neural networks. New York: John Wiley & sons, Inc.; 1998
    3. Y. Sekine, H. Okamoto, and T. Shibamoto, “Fault section estimation
    4. using cause–effect network,” in Proceedings of 2nd Symposium on Expert System Application to Power Systems, Seattle, WA, U.S.A., July 1989, pp. 277– 282.
    5.  Y. Y. Hsu, F. C. Lu, Y. Chien, J. P. Liu, J. T. Lin, H. S. Yu, and R. T. Kuo,“An expert system for locating distribution system faults,” IEEE Trans. Power Delivery, vol. 6, no. 1, pp. 366–372, Jan. 1991.
    6. K. L. Lo et al., “Extended Petri net models for fault diagnosis for substation automation,” Proc. Inst. Elect. Eng., Gen., Transm., Distrib., vol. 146, no. 3, pp. 229–234, May 1999.
    7. K. L. Butler and J. A. Momoh, “A neural network based approach for fault diagnosis in distribution networks,” in Proc. IEEE Power Engineering Society Winter Meeting, vol. 2, 2000, pp. 1275–1278.
    8. J. Sun, S. Y. Qin, and Y. H. Song, “Fault diagnosis of electric power
    9. systems based on fuzzy petri nets,” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 2053–2059, Nov. 2004.
    10. J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proc. Int. Conf. Systems, Man, Cybernetics.
    11. Piscataway, NJ, 1997, pp. 4104–4109.

Recent Article