基于免疫微粒群算法的梯级水电站水库优化调度研究
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摘要
针对当前梯级水电站水库优化调度方法中普遍存在的“维数灾”问题,本文提出一类基于免疫微粒群算法(I-PSO)的梯级水电站水库优化方法。微粒群算法(PSO)是一类随机全局优化技术,其流程简单易实现、算法依赖的参数少。I-PSO是将免疫信息处理机制和标准PSO有机结合在一起的一种改进的微粒群算法,一方面采用微粒浓度选择机制来保持微粒多样性,较好的解决了算法可能出现的局部收敛问题;另一方面通过在算法中加入疫苗接种等操作来指导寻优过程,加快了算法后期的收敛速度。经实例验证,运用I-PSO调度模型在梯级水电站水库的长期和短期调度中取得良好的优化效果,能够为解决复杂的梯级水电站优化问题提供一条有效、可行的新途径。
Since the problem of“dimension disaster”exits widely in current optimal dispatching methods of cascade hydropower station, this paper has presented a cascade hydropower station optimal method based on Immune Particle Swarm Optimization (I-PSO) Algorithm. Particle Swarm Optimization (PSO) is a stochastic global optimization algorithm, which is simple to implement and depends on fewer parameters. I-PSO is an improved PSO algorithm which combined immune information-processing mechanism and standard PSO, on the one hand the local convergence issues that may arise can be solved well by concentration of particulate selection mechanism used to maintain the diversity of particles; on the other hand, the algorithm convergent speeds up latter through adding vaccination to guide the optimization process. As the case study shows, I-PSO dispatching model obtained well optimal results by applying I-PSO dispatching model in cascade hydropower station, which provided an effective and feasible way to solve complex cascade optimization problem.
Since the problem of“dimension disaster”exits widely in current optimal dispatching methods of cascade hydropower station, this paper has presented a cascade hydropower station optimal method based on Immune Particle Swarm Optimization (I-PSO) Algorithm. Particle Swarm Optimization (PSO) is a stochastic global optimization algorithm, which is simple to implement and depends on fewer parameters. I-PSO is an improved PSO algorithm which combined immune information-processing mechanism and standard PSO, on the one hand the local convergence issues that may arise can be solved well by concentration of particulate selection mechanism used to maintain the diversity of particles; on the other hand, the algorithm convergent speeds up latter through adding vaccination to guide the optimization process. As the case study shows, I-PSO dispatching model obtained well optimal results by applying I-PSO dispatching model in cascade hydropower station, which provided an effective and feasible way to solve complex cascade optimization problem.
引文
[1]张勇传.水电能优化管理[M].武汉:华中工学院出版社, 1986
[2]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报, 1998, 18(1): 64~66
[3]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学, 1999, 17(3): 27~30
[4]王德意,南海鹏,刘栋.快速动态规划法在运行机组间有功负荷分配中的应用[J].水利水电技术, 2001(6):28~30
[5]孙昌佑,马震岳.基于遗传算法的水电站厂内经济运行模型研究[J].水电能源科学, 2004年3月:48~50
[6]张英贵.电力系统中水电厂群日优化运行[M].华中理工大学出版社, 1993年
[7]陈森林.流域水电站群短期优化调度的均匀用水最小模型[J].水电能源科学, 1999, 17(3):9~12
[8]陈森林,万俊,刘子龙等.水电系统短期优化调度的一般性准则——基本概念与数学模型[J].武汉水利电力大学学报, 1999, 32(3):34~37
[9]陈森林,万俊,刘子龙等.水电系统短期优化调度的一般性准则——优化模型求解方法及实例应用[J].武汉水利电力大学学报, 1999, 32(3):38~42
[10]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法[J].水力发电学报, 2002(2): 42~44
[11]罗予如.梯级水电厂群短期经济运行的探讨[J].水力发电, 2000(2): 57~58
[12]胡明罡,练继建.基于改进遗传算法的水电厂日优化调度方法研究[J].水力发电学报, 2004年4月
[13]张勇传.水电站经济运行原理[M].北京:中国水利电力出版社,1998
[14]叶秉如.水电站库群的年最优调度.见:优化理论在水库调度中的应用.长沙:湖南科学技术出版社, 1985.65~73
[15]董子敖.水库群调度与规划的优化理论和应用[M].济南:山东科学技术出版社,1989
[16]罗洋涛,周云,倪定远.水库群长期优化调度研究[J].水电站设计,1998,14(1): 6~10
[17]畅建霞,黄强,王义民.水电站水库优化调度几种方法的探讨[J].水电能源科学, 2000, 18(3): 19~22
[18]麻荣永著.水电站水库随机优化方法.中国水利水电出版社,2001.4
[19]伍宏中.水电站水库群径流补偿调节的非线性规划优化.水能源科学.1994年02期,84～90
[20]陈宝琳.最优化理论与算法.清华大学出版社.2000.3
[21]杨侃,陈雷.梯级水电站群调度多目标网络分析模型.水利水电科技进展.1998年03期,34～38
[22]鲁子林.水库群调度网络分析法.河海大学学报(自然科学版).1983年第4期
[23]董子敖.水库群调度与规划的优化理论和应用.济南:山东科学技术出版社,1989,12
[24]冯尚友.水资源系统工程.武汉:湖北科学技术出版社,1991
[25]华中工学院.水电站经济运行.北京:电力工业出版社,1981
[26]黄强.水能利用.北京:中国水利水电出版社,1998,10
[27]李钰心.水电站经济运行.北京:中国电力出版社,1999,4
[28]张勇传.水电站经济运行原理.北京:中国水利水电出版社,1998,10
[29]张勇传.水电能优化管理.华中工学院出版社,1986,1～314
[30]张勇传.水电系统最优控制.华中理工大学出版社,1993,129～325
[31]宗航,周建中,张勇传. POA改进算法在梯级电站优化调度中的研究和应用.计算机工程,2003,29(17):105-109
[32]路志宏.三峡梯级水库优化调度模型及其应用:[博士学位论文].武汉:华中科技大学图书馆,2003
[33]A. Turgon. Optimization short–term hydro scheduling from the principle of progressive optimality. Water Resources Research, 1981,17(6): 481-486
[34]S.M.Amado, C.C.Ribeiro. Short-term generation scheduling of hydraulic multi-reservoir multi-area interconnected systems. IEEE Trans. on Power Systems, 1987, 2(3): 758～763
[35]M.R.G.AL-Shakachi, H.D.H.AL-Hassany. Short-term hydrothermal power system unit commitment: a comparative study. Electric Machines and Power Systems. 2000, 28(10): 955～969
[36]Shi-chung Chang, Chun-Hung Chen, I-Kong Fong, et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm. IEEE Trans. on Power Systems, 1990,5(3):737～743
[37]J.Tang, P.B.Luh. Hydrothermal scheduling via extended differential dynamic programming and mixed coordination. IEEE Trans. on Power Systems,1995,10(4): 2021～2028
[38]G.X.Luo, H.Habibollahzadeh, A.Semlyen. Short-term hydrothermal dispatch detailed model and solutions. IEEE Trans. on Power Systems, 1989,4(4): 1452～1462
[39]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法.水力发电学报,2000,69(2):1～7
[40]张英贵.电力系统中水电厂群日优化运行.华中理工大学出版社.1994,1～194
[41]S.V.Vidya Sagar, D.R.Nutakki. Artificial neural networks and their application to power systems—a bibliographical survey. Electric Power Systems Research, 1993, 28(1): 67～79
[42]Singh, S.C.Srivastava. Fast approach to neural network training and its application to economic load dispatch. Electric Machines and Power Systems,1995, 23(1): 13～24
[43]朱敏,王定一.基于人工神经网络的梯级水电厂日优化运行电力系统自动化.1999,23(10):35～40
[44]J.H.Park, Y.S.Kim, K.Y.Lee. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network. IEEE Trans. Power System, 1993,8(3):1030～1038
[45]R.Naresh, J.Sharma. Hydro system scheduling using ANN approach. IEEE Trans. on Power Systems, 2000, 15(1): 388～394
[46]R.Naresh, J.Sharma. Short term hydro scheduling using two-phase neural network. Electrical Power & Energy Systems. 2002, 24(6): 583～590
[47]伍永刚.双链态遗传算法及其在电力系统和梯级水电站优化调度中的应用.[博士学位论文].华中理工大学,1997
[48]王黎,马光文.基于遗传算法的水电站调度新方法.系统工程理论与实践,1997(7),65～82
[49]P.-H.Chen and H.-C.Chang. Genetic aided scheduling of hydraulically coupled plants in hydrothermal coordination. IEEE Trans. on Power Systems,1996,11(2):975～981
[50]H.C.Chang, P.H.Chen. Hydrothermal generation scheduling package: a genetic based approach. IEE Proceedings-Generation, Transmission and Distribution, 1998, 145(4): 451～457
[51]Y.G.Wu, C.Y.Ho,D.Y.Wang. A diploid genetic approach to short-term scheduling of hydro-thermal system. IEEE Trans. on Power Systems,2000,15(4): 1268～1274
[52]A.A.EL.Desouky, R.Aggarwal, M.M.Elkateb, F.Li. Advanced hybrid genetic algorithm for short-term generation scheduling. IEE Proceedings-Generation, Transmission and Distribution, 2001, 148(6):511～517
[53]郑毅,吴斌.由鸟群和蚂蚁想到的—基于主体的仿真和群集智能的研究[J].微电脑世界, 2001, (16):89~91
[54]彭喜元,彭宇,戴毓丰.群智能理论及应用[J].电子学报, 2003, 31(12): 1982~1988
[55]彭宇.群智能优化算法及理论研究[D],哈尔滨工业大学博士学位论文, 2004年
[56] Kennedy J, Eberhart R C. Particle Swarm Optimization [A]. IEEE International Conf. on Neural Networks[C]. Perth, Australia: IEEE, 1995:1942~1948
[57]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策, 2003, 18(2):129～134
[58] J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intellligence. 1997: 45~49
[59] A Ismail, A P Engelbrecht. Global Optimization Algorithms for Tranning Product Unit Neural Networks. International Joint Conference on Neural Networks. 2000, I:132~137
[60] I. Kassabalidis, M.A. El-Sharkawi, R.J. Marks, P. Arabshahi, A.A. Gray. Adaptive-SDR: Adaptive Swarm-Based Distributed Routing. Proceedings of the 2002 International Joint Conference on Neural Networks. 2002, 1: 351~354
[61] H. Yoshida, K. Kawata, Y. Fukuyama, Nakanishi. A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability.Proc. Intl. Conf. on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil, 2001: 117~121
[62] J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intellligence. 1997: 45~49
[63] A Ismail, A P Engelbrecht. Global Optimization Algorithms for Tranning Product Unit Neural Networks. International Joint Conference on Neural Networks. 2000, I:132~137
[64] I. Kassabalidis, M.A. El-Sharkawi, R.J. Marks, P. Arabshahi, A.A. Gray.Adaptive-SDR: Adaptive Swarm-Based Distributed Routing. Proceedings of the 2002 International Joint Conference on Neural Networks. 2002, 1: 351~354
[65] H. Yoshida, K. Kawata, Y. Fukuyama, Nakanishi. A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability.Proc. Intl. Conf. on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil, 2001: 117~121
[66] Sensarma PS, Rahmani M. A Comprehensive Method for Optima Expansion Planning using Particle Swarm Optimization[A]. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference[C]. New York, USA, 2002:1317~1322
[67] Zwe-Lee Gaing. Discrete Particle Swarm Optimization Algorithm for Unit Commitment[A]. IEEE Power Engineering Society General Meeting[C]. Ontario, Canada, 2003: 418~424
[68] EI-Gallad A, EI-Hawary M. Particle Swarm Optimizer for Constrained Economic Dispatch with Prohibited Operating Zones[A]. Canadian Conference on Electrical and Computer Engineering [C]. Manitoba, Canada, 2002: 78~81
[69] Jong-Bae park, Ki-Song Lee. Economic Load Dispatch for Non-smooth Cost Functions using Particle Swarm Optimization[A]. IEEE Power Engineering Society General Meeting[C]. Ontario, Canada, 2003: 938~943
[70] Zhang W, Liu Y. An adaptive PSO algorithm for Reactive Power Optimization [A]. Advances in Power System Control Operation and Management, S6 Application of Artificial Intelligence Technique[C]. Hong Kong, China, 2003: 1534~1538
[71] Abido M A. Optimal Design of Power System Stabilizers using Particle Swarm Optimization[J].IEEE Trans on Energy Conversion, 2002, 17(3): 406~413
[72]唐剑东,熊信艮,吴耀武等.基于改进PSO算法的电力系统无功优化[J].电力自动化设备, 2004, 24(7): 81~84
[73]汪新星,张明.基于改进微粒群算法的水火电力系统短期发电计划优化[J].电网技术, 2004, 28(12): 16~19
[74]袁晓辉,王乘,张勇传等.粒子群优化算法在电力系统中的应用[J].电网技术, 2004, 28(19): 14~19
[75]赵波,曹一家.电力系统机组组合问题的改进粒子群优化算法[J].电网技术, 2004年, 28(21): 6~10
[76]李辉改进微粒群算法在水电站优化调度中的应用天津大学硕士毕业论文2005
[77]李崇浩,纪昌明,李文武.微粒群算法在水电站厂内经济运行中的应用研究,水利水电技术, 2006, 37(1): 88～91
[78]李崇浩,纪昌明,李文武.改进微粒群算法及其在水库优化调度上的应用,中国农村水利水电, 2006年第2期: 54~56
[79]李崇浩,纪昌明,李文武.基于微粒群算法的梯级水电系统短期优化调度研究,水力发电学报, 2006, 25(2):94~98
[80]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004(6): 4～6
[81]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3): 416～420
[82]曹俊,朱如鹏.一种基于自适应算子和移民策略相结合的遗传算法[J].机械科学与技术, 2003.11, 22(增刊), 64～66
[83]王磊,潘进,焦李成,免疫算法[J].电子学报. 2000:28 (7):74—78
[84]王磊,潘进,焦李成,免疫规划[J].计算法学报. 2000:23 (7):806—812
[85]高文豪.大系统最优化[M].北京:水利电力出版社,1991年
[86]冯尚友.水资源系统工程[M].武汉:湖北科学技术出版社,1991年
[87]李钰心.水电站经济运行[M].北京:中国电力出版社, 1999
[88]刑文训,谢金星.现代优化方法.清华大学出版社.2000:40-87.
[89]刘鑫卿,张士军.梯级水电站流量滞后性影响研究.华中理工大学学报.1999,5(27):46-48.
[90]周玉琴.三峡梯级与清江梯级联合调度研究[D].武汉大学硕士学位论文, 2005
[2]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报, 1998, 18(1): 64~66
[3]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学, 1999, 17(3): 27~30
[4]王德意,南海鹏,刘栋.快速动态规划法在运行机组间有功负荷分配中的应用[J].水利水电技术, 2001(6):28~30
[5]孙昌佑,马震岳.基于遗传算法的水电站厂内经济运行模型研究[J].水电能源科学, 2004年3月:48~50
[6]张英贵.电力系统中水电厂群日优化运行[M].华中理工大学出版社, 1993年
[7]陈森林.流域水电站群短期优化调度的均匀用水最小模型[J].水电能源科学, 1999, 17(3):9~12
[8]陈森林,万俊,刘子龙等.水电系统短期优化调度的一般性准则——基本概念与数学模型[J].武汉水利电力大学学报, 1999, 32(3):34~37
[9]陈森林,万俊,刘子龙等.水电系统短期优化调度的一般性准则——优化模型求解方法及实例应用[J].武汉水利电力大学学报, 1999, 32(3):38~42
[10]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法[J].水力发电学报, 2002(2): 42~44
[11]罗予如.梯级水电厂群短期经济运行的探讨[J].水力发电, 2000(2): 57~58
[12]胡明罡,练继建.基于改进遗传算法的水电厂日优化调度方法研究[J].水力发电学报, 2004年4月
[13]张勇传.水电站经济运行原理[M].北京:中国水利电力出版社,1998
[14]叶秉如.水电站库群的年最优调度.见:优化理论在水库调度中的应用.长沙:湖南科学技术出版社, 1985.65~73
[15]董子敖.水库群调度与规划的优化理论和应用[M].济南:山东科学技术出版社,1989
[16]罗洋涛,周云,倪定远.水库群长期优化调度研究[J].水电站设计,1998,14(1): 6~10
[17]畅建霞,黄强,王义民.水电站水库优化调度几种方法的探讨[J].水电能源科学, 2000, 18(3): 19~22
[18]麻荣永著.水电站水库随机优化方法.中国水利水电出版社,2001.4
[19]伍宏中.水电站水库群径流补偿调节的非线性规划优化.水能源科学.1994年02期,84～90
[20]陈宝琳.最优化理论与算法.清华大学出版社.2000.3
[21]杨侃,陈雷.梯级水电站群调度多目标网络分析模型.水利水电科技进展.1998年03期,34～38
[22]鲁子林.水库群调度网络分析法.河海大学学报(自然科学版).1983年第4期
[23]董子敖.水库群调度与规划的优化理论和应用.济南:山东科学技术出版社,1989,12
[24]冯尚友.水资源系统工程.武汉:湖北科学技术出版社,1991
[25]华中工学院.水电站经济运行.北京:电力工业出版社,1981
[26]黄强.水能利用.北京:中国水利水电出版社,1998,10
[27]李钰心.水电站经济运行.北京:中国电力出版社,1999,4
[28]张勇传.水电站经济运行原理.北京:中国水利水电出版社,1998,10
[29]张勇传.水电能优化管理.华中工学院出版社,1986,1～314
[30]张勇传.水电系统最优控制.华中理工大学出版社,1993,129～325
[31]宗航,周建中,张勇传. POA改进算法在梯级电站优化调度中的研究和应用.计算机工程,2003,29(17):105-109
[32]路志宏.三峡梯级水库优化调度模型及其应用:[博士学位论文].武汉:华中科技大学图书馆,2003
[33]A. Turgon. Optimization short–term hydro scheduling from the principle of progressive optimality. Water Resources Research, 1981,17(6): 481-486
[34]S.M.Amado, C.C.Ribeiro. Short-term generation scheduling of hydraulic multi-reservoir multi-area interconnected systems. IEEE Trans. on Power Systems, 1987, 2(3): 758～763
[35]M.R.G.AL-Shakachi, H.D.H.AL-Hassany. Short-term hydrothermal power system unit commitment: a comparative study. Electric Machines and Power Systems. 2000, 28(10): 955～969
[36]Shi-chung Chang, Chun-Hung Chen, I-Kong Fong, et al. Hydroelectric generation scheduling with an effective differential dynamic programming algorithm. IEEE Trans. on Power Systems, 1990,5(3):737～743
[37]J.Tang, P.B.Luh. Hydrothermal scheduling via extended differential dynamic programming and mixed coordination. IEEE Trans. on Power Systems,1995,10(4): 2021～2028
[38]G.X.Luo, H.Habibollahzadeh, A.Semlyen. Short-term hydrothermal dispatch detailed model and solutions. IEEE Trans. on Power Systems, 1989,4(4): 1452～1462
[39]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法.水力发电学报,2000,69(2):1～7
[40]张英贵.电力系统中水电厂群日优化运行.华中理工大学出版社.1994,1～194
[41]S.V.Vidya Sagar, D.R.Nutakki. Artificial neural networks and their application to power systems—a bibliographical survey. Electric Power Systems Research, 1993, 28(1): 67～79
[42]Singh, S.C.Srivastava. Fast approach to neural network training and its application to economic load dispatch. Electric Machines and Power Systems,1995, 23(1): 13～24
[43]朱敏,王定一.基于人工神经网络的梯级水电厂日优化运行电力系统自动化.1999,23(10):35～40
[44]J.H.Park, Y.S.Kim, K.Y.Lee. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network. IEEE Trans. Power System, 1993,8(3):1030～1038
[45]R.Naresh, J.Sharma. Hydro system scheduling using ANN approach. IEEE Trans. on Power Systems, 2000, 15(1): 388～394
[46]R.Naresh, J.Sharma. Short term hydro scheduling using two-phase neural network. Electrical Power & Energy Systems. 2002, 24(6): 583～590
[47]伍永刚.双链态遗传算法及其在电力系统和梯级水电站优化调度中的应用.[博士学位论文].华中理工大学,1997
[48]王黎,马光文.基于遗传算法的水电站调度新方法.系统工程理论与实践,1997(7),65～82
[49]P.-H.Chen and H.-C.Chang. Genetic aided scheduling of hydraulically coupled plants in hydrothermal coordination. IEEE Trans. on Power Systems,1996,11(2):975～981
[50]H.C.Chang, P.H.Chen. Hydrothermal generation scheduling package: a genetic based approach. IEE Proceedings-Generation, Transmission and Distribution, 1998, 145(4): 451～457
[51]Y.G.Wu, C.Y.Ho,D.Y.Wang. A diploid genetic approach to short-term scheduling of hydro-thermal system. IEEE Trans. on Power Systems,2000,15(4): 1268～1274
[52]A.A.EL.Desouky, R.Aggarwal, M.M.Elkateb, F.Li. Advanced hybrid genetic algorithm for short-term generation scheduling. IEE Proceedings-Generation, Transmission and Distribution, 2001, 148(6):511～517
[53]郑毅,吴斌.由鸟群和蚂蚁想到的—基于主体的仿真和群集智能的研究[J].微电脑世界, 2001, (16):89~91
[54]彭喜元,彭宇,戴毓丰.群智能理论及应用[J].电子学报, 2003, 31(12): 1982~1988
[55]彭宇.群智能优化算法及理论研究[D],哈尔滨工业大学博士学位论文, 2004年
[56] Kennedy J, Eberhart R C. Particle Swarm Optimization [A]. IEEE International Conf. on Neural Networks[C]. Perth, Australia: IEEE, 1995:1942~1948
[57]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策, 2003, 18(2):129～134
[58] J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intellligence. 1997: 45~49
[59] A Ismail, A P Engelbrecht. Global Optimization Algorithms for Tranning Product Unit Neural Networks. International Joint Conference on Neural Networks. 2000, I:132~137
[60] I. Kassabalidis, M.A. El-Sharkawi, R.J. Marks, P. Arabshahi, A.A. Gray. Adaptive-SDR: Adaptive Swarm-Based Distributed Routing. Proceedings of the 2002 International Joint Conference on Neural Networks. 2002, 1: 351~354
[61] H. Yoshida, K. Kawata, Y. Fukuyama, Nakanishi. A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability.Proc. Intl. Conf. on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil, 2001: 117~121
[62] J.Salerno. Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model.Proceedings of Ninth IEEE International Conference on Tools with Artificial Intellligence. 1997: 45~49
[63] A Ismail, A P Engelbrecht. Global Optimization Algorithms for Tranning Product Unit Neural Networks. International Joint Conference on Neural Networks. 2000, I:132~137
[64] I. Kassabalidis, M.A. El-Sharkawi, R.J. Marks, P. Arabshahi, A.A. Gray.Adaptive-SDR: Adaptive Swarm-Based Distributed Routing. Proceedings of the 2002 International Joint Conference on Neural Networks. 2002, 1: 351~354
[65] H. Yoshida, K. Kawata, Y. Fukuyama, Nakanishi. A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability.Proc. Intl. Conf. on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil, 2001: 117~121
[66] Sensarma PS, Rahmani M. A Comprehensive Method for Optima Expansion Planning using Particle Swarm Optimization[A]. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference[C]. New York, USA, 2002:1317~1322
[67] Zwe-Lee Gaing. Discrete Particle Swarm Optimization Algorithm for Unit Commitment[A]. IEEE Power Engineering Society General Meeting[C]. Ontario, Canada, 2003: 418~424
[68] EI-Gallad A, EI-Hawary M. Particle Swarm Optimizer for Constrained Economic Dispatch with Prohibited Operating Zones[A]. Canadian Conference on Electrical and Computer Engineering [C]. Manitoba, Canada, 2002: 78~81
[69] Jong-Bae park, Ki-Song Lee. Economic Load Dispatch for Non-smooth Cost Functions using Particle Swarm Optimization[A]. IEEE Power Engineering Society General Meeting[C]. Ontario, Canada, 2003: 938~943
[70] Zhang W, Liu Y. An adaptive PSO algorithm for Reactive Power Optimization [A]. Advances in Power System Control Operation and Management, S6 Application of Artificial Intelligence Technique[C]. Hong Kong, China, 2003: 1534~1538
[71] Abido M A. Optimal Design of Power System Stabilizers using Particle Swarm Optimization[J].IEEE Trans on Energy Conversion, 2002, 17(3): 406~413
[72]唐剑东,熊信艮,吴耀武等.基于改进PSO算法的电力系统无功优化[J].电力自动化设备, 2004, 24(7): 81~84
[73]汪新星,张明.基于改进微粒群算法的水火电力系统短期发电计划优化[J].电网技术, 2004, 28(12): 16~19
[74]袁晓辉,王乘,张勇传等.粒子群优化算法在电力系统中的应用[J].电网技术, 2004, 28(19): 14~19
[75]赵波,曹一家.电力系统机组组合问题的改进粒子群优化算法[J].电网技术, 2004年, 28(21): 6~10
[76]李辉改进微粒群算法在水电站优化调度中的应用天津大学硕士毕业论文2005
[77]李崇浩,纪昌明,李文武.微粒群算法在水电站厂内经济运行中的应用研究,水利水电技术, 2006, 37(1): 88～91
[78]李崇浩,纪昌明,李文武.改进微粒群算法及其在水库优化调度上的应用,中国农村水利水电, 2006年第2期: 54~56
[79]李崇浩,纪昌明,李文武.基于微粒群算法的梯级水电系统短期优化调度研究,水力发电学报, 2006, 25(2):94~98
[80]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004(6): 4～6
[81]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3): 416～420
[82]曹俊,朱如鹏.一种基于自适应算子和移民策略相结合的遗传算法[J].机械科学与技术, 2003.11, 22(增刊), 64～66
[83]王磊,潘进,焦李成,免疫算法[J].电子学报. 2000:28 (7):74—78
[84]王磊,潘进,焦李成,免疫规划[J].计算法学报. 2000:23 (7):806—812
[85]高文豪.大系统最优化[M].北京:水利电力出版社,1991年
[86]冯尚友.水资源系统工程[M].武汉:湖北科学技术出版社,1991年
[87]李钰心.水电站经济运行[M].北京:中国电力出版社, 1999
[88]刑文训,谢金星.现代优化方法.清华大学出版社.2000:40-87.
[89]刘鑫卿,张士军.梯级水电站流量滞后性影响研究.华中理工大学学报.1999,5(27):46-48.
[90]周玉琴.三峡梯级与清江梯级联合调度研究[D].武汉大学硕士学位论文, 2005