Distribution Network Reconfiguration with Distributed Generation Based on Cloud Genetic Algorithm

Abstract:

According to the traditional distribution network reconfiguration, the fault feeder with distributed generation (DG) will separate from the distribution network immediately while the network goes wrong. In order to improve the system power supply reliability and the utilization rate of DG, the new standard allows the distribution network change into island operation. This paper establishes the mathematics model of the distribution network reconfiguration with DG, the objective function and constraint conditions. The traditional genetic algorithm (GA) has the shortcomings of premature convergence and slow convergence speed to solve this nonlinear optimization problem. This paper applies the cloud genetic algorithm (CGA) to solve the network reconfiguration problem. The Case study on IEEE33 test system shows that the algorithm is reasonable and effective.

• Periodical: Advanced Materials Research (Volume 529)
• Pages: 306-310
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.529.306
• Online since: June 2012
• Authors: Long Hui Liu, Yan Wang, Shu Jun Yao, Lu Yao Ma, Jing Yang
• Keywords: Cloud Genetic Algorithm, Distributed Generation (DG), Distribution Network Reconfiguration

References

[1] Y. Tian, T. Lin, M. Zhang and X.L. Xu, A New Strategy of Distribution System Service Restoration Using Distributed Generation, IEEE Sustainable Power Generation and Supply, (2009).

DOI: 10.1109/supergen.2009.5348228

[2] Y.W. Liang, Z.J. Hu and Y.P. Chen, A Surver of Distributed Generation and Its Application in Power System, Power System Technology 2003. 12.

[3] Y.Y. Hsu and H. C. Kuo, A heuristic based fuzzy reasoning approach for distribution system service restoration, IEEE Trans. Power Delivery, vol. 9, p.948–953, Apr. (1994).

DOI: 10.1109/61.296277

[4] Zuhaila Mat Yasin, Titik Khawa Abdul Rahman, Influence of Distributed Generation on Distribution Network Performance during Network Reconfiguration for Service Restoration, IEEE First International Power and Energy Coference PECon 2006 November 28-29, (2006).

DOI: 10.1109/pecon.2006.346715

[5] Yogendra Kumar, Biswarup Das and Jaydev Sharma, Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System with Priority Customers, IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 23, NO. 1, Jan (2008).

DOI: 10.1109/tpwrd.2007.905412

[6] ZuhMat Yasin, Titik Khawa Abdul Rahman, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Mara, Service Restoration in Distribution Network with Distributed Generation, IEEE Student Conference on Research and Development (SCOReD 2006), 27-28 June, (2006).

DOI: 10.1109/scored.2006.4339338

[7] H.F. Li, Y. Zhang, G.J. Qian and J. Huang. Distribution network Fault Recovery Reconstruction Algorithm. 2001, 25 (8) : 34-37. (In Chinese).

[8] S.S. Qing, Z.R. Liang, W.Q. Zhang and Y.H. Yang. Region Based on Genetic Algorithm Outage Recovery. Power Systems, 2001, 25 (8) : 53-55(In Chinese).

[9] L.F. WU, Function Optimization Based on Cloud Genetic Algorithm., Computer Knownogy and Tecnology. Vol. 7, No. 28, October 2011. (In Chinese).