TDET  >> Vol. 2 No. 2 (June 2013)

    Reactive Power and Voltage Control in a Distribution System by Quantum-Inspired Gravitational Search Algorithm

  • 全文下载: PDF(697KB) HTML    PP.48-56   DOI: 10.12677/TDET.2013.22009  
  • 下载量: 1,288  浏览量: 5,988  



配电系统虚功率与电压控制有载分接头电容器量子计算引力搜寻法Distribution System; Reactive Power and Voltage Control; ULTC; Capacitor; Quantum Computing; Gravitational Search Algorithm

本文是藉由调度配电系统之主变压器有载分接头和电容器来控制虚功率与电压。其主要目的是降低配电系统中的馈电线损失及改善汇流排的电压偏移量,以达到节约能源、并使系统电压能够操作在稳定的范围内。利用虚功率与电压控制设备,加以适当的规划调度,来控制电压及提供虚功率,同时也必须满足限制条件,其限制条件包含主变压器有载分接头及所有电容器一天之中的最大总切换次需在限制的次内,及各汇流排的电压需维持在其限定的范围内,以及电力守恒与线输电容限制。在满足限制式的条件下,配电网路能供给用户稳定的电压并提升供电品质。本文采用量子启发式引力搜寻法(Quantum-Inspired Gravitational Search Algorithm, QGSA)来作配电系统的虚功率及电压控制问题。量子启发式引力搜寻法是一种以引力搜寻法为基础,并结合量子计算观念与原理,更新解的位置,使其可以有效的搜寻到全域最佳解的算法。为了证实本文所使用的方法有效性,文中以一30个汇流排的配电系统作测试。由结果得知,本文所采用的方法确实能有效的得到令人满意的结果。

This paper investigates reactive power and voltage control in a distribution system by dispatching the main transformer ULTC and capacitors. The main purpose is to reduce the feeder loss and improve the bus voltage profile of the distribution system. The devices of reactive power and voltage control are properly dispatched to control the voltage and to provide reactive power. And the constraints that must be considered include the maximum allowable number of switching operation in a day for ULTC and each capacitor, the bus voltage limit on each bus, the line flow limit on each feeder, and the power conservation limit on each bus. Under satisfying these constraints, the distribution network can provide stable voltage to the consumers, and the quality of power supply can be enhanced. The quantum-inspired gravitational search algorithm (QGSA) is proposed for solving the reactive power and voltage control problem in a distribution system. The QGSA approach is based on the gravitational search algorithm. And the concept and principles of the quantum computing are added to update the solution, so that the global optimal solution can be efficiently found. To confirm the usefulness of the proposed method, a 30-bus distribution system is performed. It is found from the results that the proposed method can effectively get a satisfying solution.

梁瑞勋, 赖建宇, 陈一通, 曾万存. 以量子启发式引力搜寻法作配电系统的虚功率及电压控制[J]. 输配电工程与技术, 2013, 2(2): 48-56.


[1] R. H. Liang, C. K. Cheng. Dispatch of main transformer ULTC and capacitors in a distribution system. IEEE Transactions on Power Delivery, 2001, 16(4): 625-630.
[2] R. H. Liang, Y. S. Wang. Main transformer ULTC and capacitors scheduling by simulated annealing approach. International Journal of Electrical Power & Energy Systems, 2001, 23(7): 531-538.
[3] Z. Hu, X. Wang, H. Chen and G. Taylor. Volt/VAr control in distribution systems using a time-interval based approach. IEE Proceedings-Generation, Transmission and Distribution, 2003, 150(5): 548-554.
[4] G. W. Kim, K.Y. Lee. Coordination control of ULTC transformer and STATCOM based on an artificial neural network. IEEE Transactions on Power Systems, 2005, 20(2): 580-586.
[5] F. A. Viawan, D. Karlsson. Combined local and remote voltage and reactive power control in the presence of induction machine distributed generation. IEEE Transactions on Power Systems, 2007, 22(4): 2003-2012.
[6] J. Y. Park, S. R. Nam and J. K. Park. Control of a ULTC considering the dispatch schedule of capacitors in a distribution system. 2007, IEEE Transactions on Power Systems, 22(2): 755-761.
[7] A. Ulinuha, M. A. S. Masoum and S. M. Islam. Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms. IEEE Transactions on Power Delivery, 2008, 23(1): 434-441.
[8] M. B. Liu, C. A. Canizares and W. Huang. Reactive power and voltage control in distribution systems with limited switching operations. IEEE Transactions on Power Systems, 2009, 24(2): 889-899.
[9] R. H. Liang, Y. K. Chen and Y. T. Chen. Volt/Var control in a distribution system by a fuzzy optimization approach. International Journal of Electrical Power & Energy Systems, 2011, 33(2): 278-287.
[10] S. Kirkpatrick, C. D. Gelatto and M. P. Vecchi. Optimization by simulated annealing. Science, New Series, 1983, 220(4598): 671- 680.
[11] J. Kennedy, R. C. Eberhart. Particle swarm optimization. IEEE International Conference on Neural Networks, 1995, 4: 1942- 1948.
[12] M. Dorigo, V. Maniezzo and A. Colorni. The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B, 1996, 26(1): 9-41.
[13] K. V. Price. Differential evolution: A fast and simple numerical optimizer. IEEE Conference on Proceedings of North American Fuzzy Information Processing, 1996: 524-527.
[14] M. Gen, R. Cheng. Genetic Algorithms and Engineering Optimization. New York: Wiley, 2000.
[15] K. H. Han, J. H. Kim. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 2002, 6(6): 580-593.
[16] E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi. GSA: A gravitational search algorithm. Information Sciences, 2009, 179(13): 2232-2248.