Abstract:Conventional algorithms for substation locating usually need long searching time, and search results are not good, thus a novel quantum evolutionary algorithm(QEA)is led in to optimize the substation site for the first time. Further more, an improved quantum evolutionary algorithm(IQEA)is presented in this paper. IQEA improves QEA form two aspects: repair operation and evolution direction; repair operation uses greedy repair operation and evolution direction uses fitness value as an attractor, thus better population diversity can be maintained, as a result, algorithm performance is improved. The experimental results by knapsack problem shows that the improvement measures enhance the global searching capability of QEA and IQEA is superior to other optimization algorithms. What’s more, the practical example verifies the validity of the proposed method and the planning result is scientific and feasible.
[1] EL-FOULY T H M, ZEINELDIN H H, EL-SAADANY E F, et al. A new optimization model for distribution substation sitting, sizing, and timing[J]. International Journal of Electrical Power and Energy Systems, 2008, 30(5): 308-315. [2] 刘自发,张建华.基于改进多组织粒子群优化算法的配电网络变电站选址定容[J].中国电机工程学报, 2007, 27(1): 105-111. [3] 王成山,刘涛,谢莹华.基于混合遗传算法的变电站选址定容[J].电力系统自动化, 2006, 30(6): 30-34, 47. [4] 段刚,余贻鑫.电力系统NP难问题全局优化算法的研究[J].电力系统自动化, 2001, 25(5): 14-18. [5] 刘建明,李茂军.基于改进遗传算法的水电经济调度[J].电力系统及其自动化学报, 2007, 19(5): 39-44. [6] 李运华,吴宏昺,盛万兴,刘科研.分布式并行混合遗传算法在无功优化中的应用[J].电力系统及其自动化学报, 2008, 20(2): 36-41. [7] 王成山,魏海洋,肖峻,谢莹华,王凯军.变电站选址定容两阶段优化规划方法[J].电力系统自动化, 2005, 29(4): 62-66. [8] HAN K H, KIM J H. Quantum-inspired evolutionary algorithms with a new termination criterion,Hζ gate, and two-phase scheme[J]. IEEE Transaction on Evolutionary Computation, 2004, 8(4): 156-169. [9] LI Y Y, ZHAO J J, JIAO L C. Quantum- Inspired evolutionary multicast algorithm[A]. Proceeding of 2009 IEEE International Conference on Systems, Man, and Cybernetics[C]. USA: IEEE press, 2009. 1496-1501. [10] JIAO L C, LI Y Y, GONG M G, ZHANG X R. Quantum-inspired immune clonal on Systems, Man, and Cybernetics, Part B, 2008, 38(5): 1234-1253. [11] 钱洁,郑建国,张超群,王翔,阎瑞霞.量子进化算法研究现状综述[J].控制与决策, 2011, 26(3): 321-326. [12] 王凌.量子进化算法研究进展[J].控制与决策, 2008, 23(12): 1321-1326. [13] ZHANG G X. Quantum-inspired evolutionary algori- thm: a survey and empirical study[J]. Journal of Heuristics, 2011, 17(3): 303-351. [14] DEFOIN P M, STEFAN S, NIKOLA K. Quantum- inspired evolutionary algorithm: a multimodel EDA[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(6): 1218-1231. [15] 谭立湘,郭立.基于全面学习的量子分布估计算法[J].模式识别与人工智能, 2010(3): 314-319. [16] 周雅兰,王甲海,印鉴.一种基于分布估计的离散粒子群优化算法[J].电子学报, 2008, 36(6): 1242-1248. [17] VLACHOGIANNIS J G, Lee K Y. Quantum-inspired evolutionary Algorithm for real and reactive power dispatch[J]. IEEE Trans. Power Syst. 2008, 23(4): 1627-1636. [18] 钱洁,郑建国.采用群体统计学习的量子进化算法[J].西安交通大学学报, 2012, 46(2): 51-58. [19] QIN Y H, ZHANG G X, Li Y Q. A comprehensive learning quantum-inspired evolutionary algorithm[J]// Communications in Computer and Information Science, 2011, vol. 268: 151-157. [20] 董永峰,杨彦卿,宋洁,顾军华,颜威利.基于改进粒子群算法的变电站选址研究[J].继电器, 2008, 36(5): 32-35.