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大数据分析方法在厂级负荷分配中的应用

DOI: 10.13334/j.0258-8013.pcsee.2015.01.009, PP. 68-73

Keywords: 大数据,多变边界,负荷分配,动态规划,基准

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Abstract:

传统厂级负荷优化分配以火电机组煤耗曲线为依据,以供电煤耗率最低为目标。考虑到火电机组结构日益复杂,多变的边界条件和运行工况加剧了机组能耗特性的不确定性,给厂级负荷优化分配带来新问题。该文基于火电机组的海量运行数据,引入大数据分析方法,通过模糊粗糙集计算方法提高数据处理的效率,利用决策相关函数评价能耗决策的置信度,获得机组不同边界和运行工况下的能耗特性。将得到的机组供电煤耗率作为厂级负荷动态规划的依据,进而预测负荷优化分配的节煤潜力。结果表明,基于大数据分析方法的厂级负荷分配可有效降低火电厂的供电煤耗率,对火电机组的节能发电调度具有参考意义。

References

[1]  张锐,商聪,李永振.基于动态改变惯性权自适应粒子群算法的电厂负荷分配研究[J].制造业自动化,2013,35(9):90-92. Zhang Rui,Shang Cong,Li Yongzhen.Multi-objective load distribution optimization for thermal power plants based on adaptive particle swarm algorithm with dynamically changing inertia weight[J].Manufacturing Automation,2013,35(9):90-92(in Chinese).
[2]  侯云鹤,鲁丽娟,熊信艮,等.改进粒子群算法及其在电力系统经济负荷分配中的应用[J].中国电机工程学报,2004,24(7):95-100. Hou Yunhe,Lu Lijuan,Xiong Xinyin,et al.Enhanced particle swarm optimization algorithm and its application on economic dispatch of power systems[J].Proceedings of the CSEE,2004,24(7):95-100(in Chinese).
[3]  李树山,李刚,程春田,等.动态机组组合与等微增率法相结合的火电机组节能负荷分配方法[J].中国电机工程学报,2011,31(7):41-47. Li Shu Shan,Li Gang,Cheng Chuntian,et al.Thermal units’ energy conservation load dispatch method with combining dynamic unit commitment into equal incremental principle[J].Proceedings of the CSEE,2011,31(7):41-47(in Chinese).
[4]  曾德良,杨婷婷,程晓,等.数据挖掘方法在实时厂级负荷优化分配中的应用[J].中国电机工程学报,2010,30(11):109-114. Zeng Deliang,Yang Tingting,Chen Xiao,et al.Application of data mining method in real-time optimal load dispatching of power plant[J].Proceedings of the CSEE,2010,30(11):109-114(in Chinese).
[5]  国家统计局.2013年国民经济和社会发展统计公报[R].北京:国家统计局,2014. National Bureau of Statistics.The national economic and social development statistical bulletin in 2013 [R].Beijing:National Bureau of Statistics,2014(in Chinese).
[6]  蔡志江,胡亚平.基于多目标多元非线性规划模型的电力负荷预测方案设计[J].电力科技与环保,2013,29(4):5-7. Cai Zhijiang,Hu Yaping.Scheme of estimating power-load based on multi-objective multivariable non-linear programming model[J].Electric Power Technology and Environmental Protection,2013,29(4):5-7(in Chinese).
[7]  Hesamzadeh M R,Galland O,Biggar D R.Short-run economic dispatch with mathematical modelling of the adjustment cost[J].Electrical Power and Energy Systems,2014,9(18):9-18.
[8]  万文军,周克毅,胥建群,等.动态系统实现火电厂机组负荷优化分配[J].中国电机工程学报,2005,25(2):125-129. Wan Wenjun,Zhou Keyi,Xu Jianqun,et al.Dynamic system on economic dispatch among thermal power units[J].Proceedings of the CSEE,2005,25(2):126-129(in Chinese).
[9]  Allah A,Mousa A.Hybrid ant optimization system for multi objective economic emission load dispatch problem under fuzziness[J].Swarm and Evolutionary Computation,2014(18):11-21.
[10]  倪敏,陈彦桥,刘吉臻,等.基于遗传算法的火电机组负荷优化分配方法研究[J].华北电力大学学报,2006,33(5):64-67. Ni Min,Chen Yanqiao,Liu Jizhen,et al.Study on load optimal dispatching method for fossil-fired units based on genetic algorithm[J].Journal of North China Electric Power University,2006,33(5):64-67(in Chinese).
[11]  Swarup K S,Yamashiro S.A genetic algorithm approach to generator unit commitment[J].Electrical Power and Energy Systems,2003,25(9):679-687.
[12]  王友,马晓茜,刘翱.自动发电控制下的火电厂厂级负荷优化分配[J].中国电机工程学报,2008,28(14):103-107. Wang You,Ma Xiaoqian,Liu Ao.Study on plant-level optimal load distribution based on automatic generation control[J].Proceedings of the CSEE,2008,28(14):103-107(in Chinese).
[13]  O’Driscoll J.Daugelaite,Sleator R D,‘Big data’,Hadoop and cloud computing in genomics,Journal of Biomedical Informatics,Volume 46,Issue 5,2013:774-781.
[14]  Bu Y,Borkar V R,Carey M J,et al.Scaling datalog for machine learning on big data[C]//Proceedings of CORR, California,America,2012.
[15]  Herodotou H,Lim H,Luo G,et al.Starfish:A self-tuning system for big data analytics[C]//5th Biennial Conference on Innovative Data Systems Research, Conference Proceedings.North Carolina.2011.
[16]  王宁玲,杨勇平,杨志平,等.多变边界条件下火电机组能耗基准状态诊断[J].中国电机工程学报,2013,33(26):1-7. Wang Ningling,Yang Yongping,Yang Zhiping,et al.Energy-consumption benchmark diagnosis of thermal power units under varying operation boundary [J].Proceedings of the CSEE,2013,33(26): 1-7(in Chinese).
[17]  Chen D G,Zhao S Y.Local reduction of decision system with fuzzy rough sets[J].Fuzzy Sets and Systems,2010,161(13):1871-1883.
[18]  Tsang E,Zhao Suyun.Decision table reduction in KDD:fuzzy rough based approach[J].Transactions on Rough Sets,2010(6):177-188.

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