Abstract

This study presents the implementation of Improved Genetic Algorithm (IGA) to minimize the power losses in the distribution network by improving selection operator pertaining to the least losses generated from the algorithm. The major part of power losses in electrical power network was highly contributed from the distribution system. Thus, the need of restructuring the topological of distribution network configuration from its primary feeders should be considered. The switches identification within different probabilities cases for reconfiguration purposes are comprehensively implemented through the proposed algorithm. The investigation was conducted to test the proposed algorithm on the 33 radial busses system and found to give the better results in minimizing power losses and voltage profile.

Keywords:

Distribution Network Reconfiguration (DNR), distribution systems , Genetic Algorithm (GA) , Improved Genetic Algorithm (IGA) , power losses,

References

1. Cheraghi, M. and P. Ramezanpour, 2012. An efficient-fast method for determining minimum loss configuration in radial distribution networks based on sensitivity analysis. Proceeding of IEEE International Power Engineering and International Conference (PEDCO, 2012). Malacca, Malaysia, pp: 46-51.
   CrossRef    
2. Guimaraes, M.A.N., C.A. Castro and R. Romero, 2010. Distribution systems operation optimisation through reconfiguration and capacitor allocation by a dedicated genetic algorithm. IET Gener. Transm. Dis., 4(11): 1213-1222.
   CrossRef    
3. Hu, Y., N. Hua, C. Wang, J. Gong and X. Li, 2010. Research on distribution network reconfiguration. Proceeding of International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE, 2010), pp: 176-180.
   
4. Li, D.D., C. He and H.Y. Shu, 2010. Optimization of electric distribution system of large offshore wind farm with improved genetic algorithm. IEEE T. Rehabil. Eng., pp: 1-6.
   
5. Niza Samsudin, S., 2009. Electricity breakdown management in Malaysia: A case study on Tenaga Nasional Berhad. M.A. Thesis, Institute of Technology Management and Enterpreneurship, UTeM, pp: 1-26.
   
6. Ritthipakdee, A., A. Thammano and N. Premasathian, 2013. A new selection operator to improve the performance of genetic algorithm for optimization problems. Proceeding of IEEE International Conference on Mechatronics and Automation (ICMA, 2013). Takamatsu, Japan, pp: 371-375.
   CrossRef    
7. Shakerian, R., H. Tavakkoli, S.H. Kamali and M. Hidayati, 2010. Improved genetic algorithm for loss and simultaneously reliability optimization in radial distribution systems. Proceeding of 3rd International Conference on Advanced Computer Theory and Engineering, 4: 325-32.
   CrossRef    
8. Shamsudin, N.H., M.S. Momat, A.F.A. Kadir, M.F. Sulaima and M. Sulaiman 2014. An optimal distribution network reconfiguration and sizing of distributed generation using modified genetic Algorithm. Int. J. Appl. Eng. Res., 9(20): 6765-6777.
   
9. Sulaima, M.F., H. Mokhlis and H.I. Jaafar, 2013. A DNR using evolutionary PSO for power loss reduction. J. Telecommun. Electron. Comput. Eng., 5(1).
   
10. Sulaima, M.F., N.H. Shamsudin, H.I. Jaafar, W.M. Dahalan and H. Mokhlis, 2014a. A DNR and DG sizing simultaneously by using EPSO. Proceeding of 5th International Conference on Intelligent Systems Modelling and Simulation, pp: 405-410.
    CrossRef    
11. Sulaima, M.F., M.S. Shidan, W.M. Dahalan, H. Mokhlis, M.F. Baharom and H.I. Jaafar, 2014b. A 16kV distribution network reconfiguration by using evolutionaring programming for loss minimizing. Int. J. Appl. Eng. Res., 9(10): 1223-1238.
    
12. Zhao, F., L. Ge and W. Li, 2012. Application of ant-genetic algorithm in reactive power optimization of distribution network. Proceeding of Asia-Pacific Power and Energy Engineering Conference (APPEEC, 2012), pp: 1-4.
    CrossRef    
13. Zhao, H., Y. Xie, N. Zheng and G. Wang, 2009. Improved genetic algorithm for reactive power optimization of distribution system. Proceeding of 6th International Conference on Advances in Power System Control, Operation and Management, pp: 157-161.
    

Competing interests

The authors have no competing interests.

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