Academic Journal of Engineering and Technology Science, 2023, 6(4); doi: 10.25236/AJETS.2023.060401.
Kong Peican, Ai Yongle
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan, China
In order to solve the problem that the search efficiency and optimal solution quality of intelligent optimization algorithm cannot be taken into account in distribution network reconfiguration, a distribution network reconfiguration method based on improved sparrow search algorithm is proposed. Firstly, the ordered basic loop matrix is formed according to the distribution network topology and branch information, and the dynamic coordination matrix is generated with the help of the heuristic rules in the optimal flow pattern; secondly, aiming at the sparrows with different division of labor in the sparrow search algorithm, the position update rules of the explorer are improved by using the dynamic coordination matrix to prevent it from falling into local optimization. At the same time, a static coordination matrix is constructed to deeply mine the adjacent schemes of the optimal individual. Finally, the simulation results of the IEEE33 node system show that the improved sparrow search algorithm has higher optimization stability and faster convergence speed, and can meet the requirements of optimal solution quality and search efficiency when solving the distribution network reconfiguration problem.
distribution network reconfiguration; sparrow search algorithm; heuristic rule; coordination optimization matrix
Kong Peican, Ai Yongle. Distribution network reconstruction with DG based on improved sparrow search algorithm. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 4: 1-10. https://doi.org/10.25236/AJETS.2023.060401.
 D. Haughton and G. T. Heydt, “Smart distribution system design: Automatic reconfiguration for improved reliability,” in IEEE PES General Meeting, 2010, pp. 1–8.
 Sarma N, Rao K. A new 0-1 integer programming method of feeder reconfiguration for loss minimization in distribution systems[J]. Electric Power Systems Research, 1995, 33(2):125-131.
 Fan J Y, Zhang L, MCDONALD J D. Distribution network reconfiguration: Single loop optimization [J]. IEEE Transactions on Power Systems, 2002, 11(3):1643-1647.
 Shirmohammadi D, Hong H W. Reconfiguration of electric distribution networks for resistive line losses reduction [J]. IEEE Transactions on Power Delivery, 1989, 4(2):1492-1498.
 S. Goswami and S. Basu, “A new algorithm for the reconfiguration of distribution feeders for loss minimization,” IEEE Trans. Power Del., vol. 7, no. 3, pp. 1484–1491, 1999.
 A. Merlin and H. Back, “Search for a minimal-loss operating spanning tree configuration in an urban power distribution system,” in In Proc. 5th Power System Computation Conference (PSCC), 1975.
 Juang Chiafeng. "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34.2 (2004): 997-1006.
 Shi Yuhui. "Particle swarm optimization: developments, applications and resources." evolutionary computation, 2001. Proceedings of the 2001 Congress on. Vol. 1. IEEE, 2001.
 R. H. Staunton and B. Ozpineci, “Microturbine power conversion technology review,” Oak Ridge National Laboratory, 2003.
 Xue J, Shen B. A novel swarm intelligence optimization approach: Sparrow Search Algorithm[J]. Systems Science & Control Engineering, 2020, 8(1):22-34.
 Su C T, Chang C F, Chiou J P. Distribution network reconfiguration for loss reduction by ant colony search algorithm [J]. Electric Power Systems Research, 2005, 75(2): 190-199.