Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework

N. Gupta; A. Swarnkar; K.R. Niazi; R.C. Bansal

Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework

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Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework

Author(s): N. Gupta ; A. Swarnkar ; K.R. Niazi ; R.C. Bansal
DOI: 10.1049/iet-gtd.2010.0056

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Author(s): N. Gupta ¹ ; A. Swarnkar ¹ ; K.R. Niazi ¹ ; R.C. Bansal ²
- Affiliations: 1: Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India
  2: School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
Source: Volume 4, Issue 12, December 2010, p. 1288 – 1298
DOI: 10.1049/iet-gtd.2010.0056 , Print ISSN 1751-8687, Online ISSN 1751-8695

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This study presents an efficient method for the multi-objective reconfiguration of radial distribution systems in fuzzy framework using adaptive genetic algorithm. The initial population for genetic algorithm is created using a heuristic approach and the genetic operators are adapted with the help of graph theory to generate feasible individuals. This avoids tedious mesh check and hence reduces the computational burden. The effectiveness of the proposed method is demonstrated on 70-bus test system and 136-bus real distribution system. The simulation results show that the proposed method is efficient and promising for multi-objective reconfiguration of radial distribution systems.

References

1. 1)
  - J.A. Martín , A.J. Gil . A new heuristic approach for distribution systems loss reduction. Electr. Power Syst. Res. , 11 , 1953 - 1958
2. 2)
  - S. Ching-Tzong , C. Chung-Fu , C. Ji-Pyng . Distribution network reconfiguration for loss reduction by ant colony search algorithm. Electr. Power Syst. Res. , 190 - 199
3. 3)
  - Z.L. Gaing . Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. , 3 , 1187 - 1195
4. 4)
  - E.M. Carreno , R. Romero , A. Padilha-Feltrin . An efficient codification to solve distribution network reconfiguration for loss reduction problem. IEEE Trans. Power Syst. , 4 , 1542 - 1551
5. 5)
  - J.H. Holland . (1975) Adaptation in natural and artificial systems.
6. 6)
  - G.W. Stagg , A.H. Ei-Abaid . (1968) Computer methods in power systems analysis.
7. 7)
  - Y.-Y. Hong , S.-Y. Ho . Determination of network configuration considering multiobjetive in distribution systems using genetic algorithms. IEEE Trans. Power Syst. , 2 , 1062 - 1069
8. 8)
  - I.J. Ramirez-Rosado , J.A. Dominguez-Navarro . New multi-objective Tabu search algorithm for fuzzy optimal planning of power distribution systems. IEEE Trans. Power Syst. , 1 , 224 - 233
9. 9)
  - S.K. Goswami , S.K. Basu . A new algorithm for the reconfiguration of distribution feeders for loss minimization. IEEE Trans. Power Deliv. , 3 , 1482 - 1491
10. 10)
  - H.-D. Chiang , R. Jean-Jumeau . Optimal network reconfigurations in distribution systems - part 2: solution algorithms and numerical results. IEEE Trans. Power Deliv. , 3 , 1568 - 1574
11. 11)
  - K. Prasad , R. Ranjan , N.C. Sahoo , A. Chaturvedi . Optimal configuration of radial distribution system using fuzzy mutated genetic algorithm. IEEE Trans. Power Deliv. , 2 , 1211 - 1213
12. 12)
  - V.N. Gohokar , M.K. Khedkar , G.M. Dhole . Formulation of distribution reconfiguration problem using network topology: a generalized approach. Electr. Power Syst. Res. , 2 , 304 - 310
13. 13)
  - J.Z. Zhu . Optimal reconfiguration of electric distribution network using refined genetic algorithm. Electr. Power Syst. Res. , 37 - 42
14. 14)
  - M.R. Irving , W.P. Luan , J.S. Daniel . Supply restoration in distribution networks using a genetic algorithm. Int. J. Electr. Power Energy Syst. , 6 , 447 - 457
15. 15)
  - D.E. Goldberg . (1999) Genetic algorithms in search, optimization and machine learning.
16. 16)
  - Y.C. Huang . Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration. IEE Gener. Transm. Distrib. , 5 , 615 - 620
17. 17)
  - S. Civanlar , J.J. Grainger , H. Yin , S.S.H. Lee . Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Del. , 1217 - 1223
18. 18)
  - D. Shirmohammadi , H.W. Hong . Reconfiguration for electric distribution networks for resistive line loss reduction. IEEE Trans. Power Del. , 1492 - 1498
19. 19)
  - W.M. Lin , F.S. Cheng , M.T. Tsay . Distribution feeder reconfiguration with refined genetic algorithm. IEE Proc. Gener. Transm. Distrib. , 6 , 349 - 354
20. 20)
  - D. Das . A fuzzy multiobjective approach for network reconfiguration of distribution systems. IEEE Trans. Power Deliv. , 1 , 202 - 209
21. 21)
  - M.E. Baran , F.F. Wu . Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Del. , 1401 - 1407
22. 22)
  - K. Nara , A. Shiose , M. Kiagawa , T. Ishihara . Implementation of genetic algorithm for distribution system loss minimum reconfiguration. IEEE Trans. Power Syst. , 3 , 1044 - 1051
23. 23)
  - A. Ahuja , S. Das , A. Pahwa . An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Trans. Power Syst. , 3 , 1101 - 1111
24. 24)
  - J. Mendoza , R. Lopez , D. Morales , E. Lopez , P. Dessante , R. Moraga . Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application. IEEE Trans. Power Syst. , 2 , 948 - 954
25. 25)
  - C.H. Lin , C.S. Chen , C.J. Wu , M.S. Kang . Application of immune algorithm to optimal switching operation for distribution loss minimization and load balance. IEE Gener. Transm. Distrib. , 2 , 183 - 189
26. 26)
  - H. Falaghi , M.R. Haghifam , C. Singh . Ant colony optimization based method for placement of sectionalizing switches in distribution networks using a fuzzy multi-objective approach. IEEE Trans. Power Deliv. , 1 , 268 - 276
27. 27)
  - B. Enacheanu , B. Raison , R. Caire , O. Devaux , W. Bienia , N. HadjSaid . Radial network reconfiguration using genetic algorithm based on the matroid theory. IEEE Trans. Power Syst. , 1 , 186 - 195
28. 28)
  - Merlin, A., Back, H.: `Search for a minimum-loss operating spanning tree configuration in an urban power distribution', Proc. Fifth Power System Computation Conf., 1975, Cambridge, UK, p. 1–18.
29. 29)
  - Zhang, C., Zhang, J., Gu, X.: `The application of hybrid genetic particle swarm optimization algorithm in the distribution network reconfigurations multi-objective optimization', Third Int. Conf. Natural Computation (ICNC 2007), 2007, 2, p. 455–459.
30. 30)
  - Y.J. Hsiao , C.Y. Chen . Multi-objective feeder reconfiguration. IEE Gener. Transm. Distrib. , 333 - 336
31. 31)
  - A. Augugliaro , L. Dusonchet , S. Favuzza , E.R. Sanseverino . Voltage regulation and power losses minimization in automated distribution networks by an evolutionary multi-objective approach. IEEE Trans. Power Syst. , 3 , 1516 - 1527

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Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework

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