Genetic algorithms to solve the power system restoration planning problem
Authors: 
Adelmo L. Cechin, José V. Canto Dos Santos, Carlos A. Mendel, Arthur T. GómezAuthors Info & Claims
Adelmo L. Cechin, José V. Canto Dos Santos, Carlos A. Mendel, Arthur T. GómezAuthors Info & ClaimsPages 261 - 268
Abstract
This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.
References
[1]
Aoki K, Kuwabara H, Satoh T, Kanezashi M (1987) Outage state optimal load allocation by automatic sectionalizing switches operation in distribution systems. IEEE T Power Deliver 2:1177---1185
[2]
Bretas A, Phadke G (2003) Artificial neural networks in power system restoration. IEEE T Power Deliver 18(4):1181---1186
[3]
Bretas N, Delben A, Carvalho A (1998) Optimal energy restoration for general distribution systems by genetic algorithm. In: 1998 international conference on power system technology, vol 1, pp. 43---47
[4]
Curcic S, Ozveren CS, Lo KL (1997) Computer-based strategy for the restoration problem in electric power distribution systems. Proc Inst Electr Eng 144:389---398
[5]
Freris L, Sasson A (1968) Investigation of the load flow problem. Proc IEEE 115(10):1459---1470
[6]
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley Longman Inc, Boston, MA
[7]
Hsu Y, Huang H (1995) Distribution systems service restoration using the artificial neural network approach and pattern recognition method. IEE Proc-C 142:251---256
[8]
Huang J, Galiana F, Vuong G (1991) Power system restoration incorporating interactive graphics and optimization. In: Power industry computer application conference, 1991. Conference proceedings, Baltimore, pp. 216---222
[9]
Kojima Y, Warashina S, Nakamura K, Matsumoto K (1989) The development of power system restoration method for a bulk power system by applying knowledge engineering techniques. IEEE T Power Syst 4(2):1228---1235
[10]
Komai K, Matsumoto K, Sakaguchi T (1988) Analysis and evaluation of expert's knowledge for power system restoration by mathematical programming method. IEEE Int Symp Circuits Syst 2(1):1895---1898
[11]
Kostic T, Germond A, Alba J (1998) Optimization and learning of load restoration strategies. Elec Power Energy Syst 20:131---140
[12]
Lindenmeyer D, Dommel H, Adibi M (2001) Power system restoration--a bibliographical survey. Elec Power Energy Syst 23:219---227
[13]
Luan W, Irving M, Daniel J (2002) Genetic algorithm for supply restoration and optimal load shedding in power system distribution networks. IEE Proc-C 149:145---151
[14]
Matos M, Ponce de Leão M, Saraiva T, Fidalgo J, Miranda V, Lopes J, Ferreira R, Pereira J, Proença M, Pinto J (2004) Metaheuristics applied to power systems in Metaheuristics: computer decision-making. Kluwer Academic Publishers, Norwell, MA, USA
[15]
Monticelli A, Garcia A, Saavedra O (1990) Fast decupled load flow: hypothesis, derivations and testing. IEEE T Power Syst 5(4):1425---1431
[16]
Nagata T, Sasaki H, Yokoyama R (1995) Power system restoration by joint usage of expert system and mathematical programming approach. IEEE T Power Syst 10(3):1473---1479
[17]
Sakaguchi T, Matsumoto K (1983) Development of a knowledge based system for power system restoration. IEEE T Power Ap Syst 102(2):320---329
[18]
Stott B, Alsac O (1974) Fast decoupled load flow. IEEE T Power Ap Syst 93:859---869
[19]
Susheela DV, Murty MN (2000) Stochastic search techniques for post-fault restoration of electrical distribution systems. Sadhana 25:45---56 (Part 1, February)
[20]
Wu F, Monticelli A (1988) Analytical tools for power system restoration--conceptual design. IEEE T Power Syst 3(1):10---16
- Genetic algorithms to solve the power system restoration planning problem
Recommendations
Using messy genetic algorithms for solving the winner determination problem
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computationThe paper presents a study on the application of messy genetic algorithms for the winner determination problem in the combinatorial auction realm. Messy genetic algorithms operate explicitly on building blocks in order to obtain good solutions. Since ...
Genetic algorithms to solve the cover printing problem
Inspired by successful application of evolutionary algorithms to solving difficult optimization problems, we explore in this paper, the applicability of genetic algorithms (GAs) to the cover printing problem, which consists in the grouping of book ...
Genetic algorithms for task scheduling problem
The scheduling and mapping of the precedence-constrained task graph to processors is considered to be the most crucial NP-complete problem in parallel and distributed computing systems. Several genetic algorithms have been developed to solve this ...
Comments
Information & Contributors
Information
Published In
Copyright © Copyright © 2009 Springer-Verlag London Limited.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 September 2009
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 17 Jan 2025