Network Reconfiguration of Unbalanced Distribution System through Hybrid Heuristic Technique
DOI:
https://doi.org/10.51983/ajes-2012.1.1.1649Keywords:
Distribution Network Reconfiguration, PGSA, greedy, Heuristic Fuzzy, Loss Reduction, Switching OperationAbstract
Electrical power distribution systems are critical links between the utility and customer. They are constructed by one of the three types: radial, open loop and network. They are usually arranged to be radial in operation to simplify over-current protection. Usually, distribution systems are designed to be most efficient at peak load demand. Utilities are constantly looking for newer technologies that enhance power delivery performance. One of the several important issues is the control of power loss. Several strategies can be employed to minimize power losses in a distribution network. They are inclusion of capacitor banks, phase balancing, feeder reconfiguration of distribution system, etc. Among these, feeder reconfiguration helps to operate the distribution system at minimum cost and at the same time improves the system reliability and security. Network reconfiguration is the process of changing the topology of distribution system by altering the open/closed status of switches to find a radial operating structure that minimizes the system real power loss while satisfying operating constraints. This paper proposes an efficient algorithm based on Plant Growth Simulation Algorithm (PGSA), Greedy and Fuzzy. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy improves the efficiency of optimization by reducing the number of load flow execution. In addition, heuristic fuzzy has been incorporated to handle constraints along with objective. With the use of proposed algorithm, the system loss has been reduced convincingly, composes proper loading at the branches and make up buses voltage within the limit and which provides solution under different conditions such as normal and abnormal conditions of the system. Furthermore, the solution algorithm is implemented through J2EE (Java 2 Enterprise Edition) architecture to reduce software couplings and to achieve software reusability. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to unbalanced standard 25- bus distribution system and modified IEEE- 125 bus distribution system.
References
S. Civanlar, J. J. Grainger, H. Yin, and S. S. H. Lee, “Distribution feeder reconfiguration for loss reduction,” IEEE Trans. Power Del., Vol. 3, No. 3, pp. 1217–1223, Jul. 1988.
Baran ME and Wu FF, ‘Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Trans. Power Del., Vol. 4,No. 1, pp. 401-1407, Jan. 1989.
K. Aoki, H. Kawabara, and M. Satoh, “An efficient algorithm for load balancing of transformers and feeders,” IEEE Trans. Power Del., Vol. 3,No. 4, pp. 1865-1872, Jul. 1988.
D. Shirmohammadi and H.W. Hong, “Reconfiguration of electric distribution networks for resistive line losses reduction,” IEEE Trans. Power Del., Vol. 4,No. 2, pp. 1492–1498, Apr. 1989.
S.K. Goswami and S.K. Basu, “A new algorithm for the reconfiguration of distribution feeders for loss minimization”, IEEE Trans. Power Del., Vol. 7, No.3, pp.1484–1490, July 1992.
H. Ying-Yi and H. Saw-Yu , “Determination of network configuration considering multiobjective in distribution systems using genetic algorithms”, IEEE Trans. on Power Sys., Vol. 20,No. 2, pp. 1062-1069, Apr. 2006.
L. Whei-Min and C. Hong-Chan, “A new approach for distribution feeder reconfiguration for loss reduction and service restoration,” IEEE Trans. Power Del., Vol. 13, No. 3, pp. 870–875, Jul. 1998.
H. Kim, Y. Ko, and K. H. Jung, “Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems,” IEEE Trans. Power Del., Vol. 8, No. 3, pp. 1356–1366, Jul. 1993.
H. Salazar, R. Gallego, and R. Romero, “Artificial neural networks and clustering techniques applied in the reconfiguration of distribution sys- tems,” IEEE Trans. Power Del., Vol. 21, No. 3, pp. 1735–1742, Jul. 2006.
C.C. Liu, S.J. Lee and S.S. Venkata, “An expert system operational aid for restoration and loss reduction of distribution systems,” IEEE Trans. Power Del., Vol. 3, No. 3, pp. 619-25, Jan. 1988.
H. C. Cheng and C. C. Ko, “Network reconfiguration in distribution systems using simulated anealing”, Elect. Power Syst. Res., Vol. 29, pp. 227-238, May 1994.
B. Venkatesh, R. Ranjan, and H. B. Gooi, “Optimal reconfiguration of radial distribution systems to maximize loadability,” IEEE Trans. Power Syst., Vol. 19,No. 1, pp. 260–266, Feb. 2004.
S. Thiruvenkadam, A. Nirmalkumar, A. Sakthivel, “MVC Architecture based Neuro-Fuzzy approach for distribution feeder reconfiguration for loss reduction and load balancing,” Proc. Int. Conf. IEEE-PES Transm., and Distrib., Chicago, USA, April. 2008.
K. Huang and H. Chin, “Distribution feeder energy conservation by using heuristics fuzzy approach,” Electrical Power and Energy Systems, Vol. 24, pp. 439-445, 2002.
Y. H. Song, G. S. Wang, A. T. Johns, and P. Y. Wang, “Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming,” Proc. Inst. Elect. Eng., Gen., Transm., Distrib., Vol. 144,No. 4, pp. 345-350, Jul. 1997.
C. B. Delbem, A. C. P. L. F. Carvalho, and N. G. Bretas, “Main chain representation for evolutionary algorithms applied to distribution system reconfiguration,” IEEE Trans. Power Syst., Vol. 20,No. 1, pp. 425–436, Feb. 2005.
T. Su and C. S. Lee, “Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution”, IEEE Trans. Power Del., Vol. 18, No. 3, pp. 1022-1027, Jul. 2003.
K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in Proc. IEEE Congr. Evolut. Comput., Edinburgh, Scotland, Sep. 2005, pp. 1785–1791.
Wang and H.Z. Cheng, “Optimization of Network configuration in Large distribution systems using plant growth simulation algorithm,” IEEE Trans. Power Syst., Vol.23, No. 1, pp. 119-126, Feb. 2008.
S. Thiruvenkadam, A. Nirmalkumar and M. Sathishkumar, “Distribution Network Optimization through Algorithm Design Techniques,” Gests Intl. Trans. On Computer Science and Engg., Vol. 57, No. 1, pp. 131-147, 2009.
S. Thiruvenkadam, A. Nirmalkumar and M. Sathishkumar, “Distribution network optimization through fusion technology,” Australian Journal of Electrical and Electronics Engg., Vol. 7, No. 2, pp. 145-152, 2009.
Borazan .V, Rajicic.D and Ackovski.R, “Minimum loss reconfiguration of unbalanced distribution networks”, IEEE Trans. Power Del., Vol. 12, No.1, pp.435–442, 1997.
Wang.J.C, Chiang.H.D, Darling.G.R, “An efficient algorithm for real time network reconfiguration in large scaled unbalance distribution systems”, IEEE Trans. on Power Sys., Vol. 11, No. 1, pp. 511-517, 1996.
K. Price, R. Storn, and J. Lampinen, Differential Evolution-A Practical Approach to Global Optimization. Berlin, Germany: Springer-Verlag, 2005.
L. Lakshminarasimman, and S. Subramanian, “Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution”, IEE Proc Gener Transm Distrib, Vol. 153, No. 6, pp. 693-700, 2006.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2012 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.