Optimal Restoration for Distribution System using PSO and ANN
Keywords:
ANN, Distribution System, PSO, Restoration,Abstract
Service restoration is an important aspect of power system design that caters the restoration of power to an un-faulted area under blackout after an emergency condition. The power system operators have the principal objective of minimizing the inconvenience occurred to consumers by isolating only the faulty area while providing the power to unaffected areas as much as possible. Their objective of providing service to customers is subjected to further constraints such as distribution system configurations, power available in the network, and the current carrying capacity of the distribution lines or feeders. Once a fault takes place, the number of customers in the blackout area mainly depends upon the effectiveness of the load restoration mechanism. Currently, the power system operators respond by implementing a pre-defined restoration schedule based on the previous human knowledge. While this may serve the purpose of power restoration to some extent. There is typically a large number of feeders with even larger number of switches in a distribution system and it is not humanly possible to restore the service to an out of service area solely based on past experiences. Many algorithms have been proposed to serve the purpose of restoration with each one having certain merits and demerits. This paper presents an effective and globally optimal restoration mechanism using Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN).References
Prajapati, Jackson, Virendra Patel, and Hemal Patel, “Load flow, short circuit and stability analysis using Matlab”, In Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on, pp. 1-5. IEEE, 2014.
Yan, Ping, A. Sekar, and P. K. Rajan, “Pattern recognition techniques applied to the classification of swing curves generated in a power system transient stability study”, In Southeastcon 2000. Proceedings of the IEEE, pp. 493-496. IEEE, 2000.
Divya, K. C., and PS Nagendra Rao. “Models for wind turbine generating systems and their application in load flow studies”, Electric Power Systems Research 76, no. 9: 844-856, 2006.
Shi, Yuhui, and Russell Eberhart, “A modified particle swarm optimizer”, In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, the 1998 IEEE International Conference on, pp. 69-73. IEEE, 1998.
Lee, Jong-Pil, Pyeong-Shik Ji, Jae-Yoon Lim, Ki-Dong Kim, Si-Woo Park, and Jung-Hoon Kim, “A load modeling using ANN for power system analysis”, In TENCON 99. Proceedings of the IEEE Region 10 Conference, vol. 2, pp. 1475-1478. IEEE, 1999.
Evolving Artificial Neural Networks Xin Yao, Senior Member, IEEE, Proceedings Of The IEEE, Vol. 87, No. 9, September 1999
Jagpreet Singh and Rajni Bala, "A Case Study: Comparison of Newton-Raphson and Gauss-Seidal Load Flow Solution Techniques in Distributed Transmission and Generation Electricity Networks", International Journal of Advanced Electrical and Electronics Engineering (IJAEEE), Volume-5 Issue-1, 2016
Kennedy, James, “The particle swarm: social adaptation of knowledge”, In Evolutionary Computation, 1997, IEEE International Conference on, pp. 303-308. IEEE, 1997.
Van Den Bergh, Frans, "An analysis of particle swarm optimizers", Ph.D. diss., University of Pretoria, 2007.
Clerc, Maurice, and James Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space”, IEEE Transactions on Evolutionary Computation 6, no. 1 : 58-73, 2002.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.