Novel SPDU Big Data Scheduling Algorithm Based on Grey Fuzzy Prediction

doi:10.11930/j.issn.1004-9649.201708074

Abstract

Abstract: For the problems existing in smart power distribution and utilization (SPDU) data service system, such as big data size, difficult information retrieval and long span scheduling, a novel SPDU big data scheduling algorithm based on grey fuzzy prediction (SGFP) is presented. In order to address the data access bottlenecks and long span scheduling of SPDU big data systems, the SGFP utilizes the grey fuzzy methods to mine the requirements of clients and management personals, and to make pre-access and scheduling of the moving data in several data servers. The SGFP is tested in an interactive community client service system. The simulation result shows that compared to the traditional algorithms, the SGFP has better response speed and accuracy, as well as lower system resource cost.

Key words: smart power distribution and utilization(SPDU), big data, data scheduling, grey prediction, client service

CLC Number:

TM769

ZHOU Shengqi, JIA Yajun, ZHU Hua. Novel SPDU Big Data Scheduling Algorithm Based on Grey Fuzzy Prediction[J]. Electric Power, 2018, 51(10): 103-110.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201708074

https://www.electricpower.com.cn/EN/Y2018/V51/I10/103

References

[1] 王守相, 梁栋, 葛磊蛟. 智能配电网态势感知和态势利导关键技术[J]. 电力系统自动化, 2016, 40(12):2-8 WANG Shouxiang, LIANG Dong, GE Leijiao. Key technologies of situation awareness and orientation for smart distribution system[J]. Automation of Electric Power Systems, 2016, 40(12):2-8
[2] 董旭柱, 吴争荣, 刘志文, 等. 智能配电网研究热点[J]. 南方电网技术, 2016, 10(5):1-9 DONG Xuzhu, WU Zhengrong, LIU Zhiwen, et al. Research focuses on smart distribution systems[J]. Southern Power System Technology, 2016, 10(5):1-9
[3] 郭建成, 钱静, 陈光, 等. 智能配电网调度控制系统技术方案[J]. 电力系统自动化, 2015, 39(1):206-212 GUO Jiancheng, QIAN Jing, CHEN Guang, et al. Technical scheme of smart distribution grid dispatching and control systems[J]. Automation of Electric Power Systems, 2015, 39(1):206-212
[4] 葛磊蛟, 王守相, 瞿海妮. 智能配用电大数据存储架构设计[J]. 电力系统自动化, 2016, 36(6):194-202 GE Leijiao, WANG Shouxiang, QU Haini. Design of storage framework for big data of SPDU[J]. Automation of Electric Power Systems, 2016, 36(6):194-202
[5] 刘广一, 朱文东, 陈金祥, 等. 智能电网大数据的特点、应用场景与分析平台[J]. 南方电网技术, 2016, 10(5):102-110 LIU Guangyi, ZHU Wendong, CHEN Jinxiang, et al. Characteristics, application scenarios and analysis platform technology of smart grid big data[J]. Southern Power System Technology, 2016, 10(5):102-110
[6] 宋杰, 谢海宁, 杨增辉. 基于多源异构数据挖掘的配电网故障信息统计分析[J]. 电力系统保护与控制, 2016, 44(3):141-147 SONG Jie, XIE Haining, YANG Zenghui. Statistical analysis of the distribution fault based on multiple source and isomerism data mining technology[J]. Power System Protection and Control, 2016, 44(3):141-147
[7] 王守相, 张卫涛, 葛磊蛟. 智能配电网统一模型云与应用服务技术系统[J]. 电力自动化设备, 2015, 35(2):49-54 WANG Shouxiang, ZHANG Weitao, GE Leijia. Unified model cloud and application service system for smart distribution network[J]. Electric Power Automation Equipment, 2015, 35(2):49-54
[8] 丁杰, 奚后玮, 韩海韵, 等. 面向智能电网的数据密集型云存储策略[J]. 电力系统自动化, 2012, 36(12):66-70 DING Jie, XI Houwei, HAN Haiyun, et al. A smart grid-oriented data placement strategy for data-intensive cloud environment[J]. Automation of Electric Power Systems, 2012, 36(12):66-70
[9] 于永军, 祁晓笑, 南东亮. 大数据在配电网低电压评估中的应用[J]. 电气技术, 2015(1):92-94 YU Yongjun, QI Xiaoxiao, NAN Dongliang. Big data applications in low-voltage distribution network to assess[J]. Electrical Engineering, 2015(1):92-94
[10] 马其燕, 秦立军. 智能配电网关键技术[J]. 现代电力, 2010, 27(2):39-46 MA Qiyan, QIN Lijun. Key technologies of smart distribution grid[J]. Modern Electric Power, 2010, 27(2):39-46
[11] MAYILVAGANAN M, SABITHA M. A cloud-based architecture for big-data analytics in smart grid:A proposal[C]//Proc. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2013:1-4.
[12] AHMED H. Applying big data analytics for energy efficiency[D]. Finland:Aalto University, 2014.
[13] ZHENG Zhigao, WANG Ping, LIU Jing, et al. Real-time big data processing framework:challenges and solutions[J]. Applied Mathematics & Information Sciences, 2015, 9(6):3169-3190.
[14] LIU Guangyi, YU Yang, GAO Feng. Research of smart distribution network big data model[C]//The 23th International Conference on Electricity Distribution, 2015:15-18.
[15] DIVYA V, GRACE P T. Big data information management of smart grid in cloud computing[C]//International Conference of Emerging Technology in Computer Science & Electronics, 2016:71-74.
[16] LILY M. A big data pilot project with smart meter data[C]//Proceedings of Statistics Canada Symposium, 2014:177-185.
[17] JANINA P. Cloud computing and smart grids[J]. Database Systems Journal, 2012, 3(3):57-66.
[18] 梁海峰, 林嘉麟, 李鹏. 含分布式能源直流配电网的优化调度[J]. 中国电力, 2016, 49(3):123-127, 159 LIANG Haifeng, LIN Jialin, LI Peng. The optimal scheduling of distributed energy DC power distribution network[J]. Electric Power, 2016, 49(3):123-127, 159
[19] 宁剑. 基于电网分区辨识的静态安全在线校正策略改进及应用[J]. 中国电力, 2017, 50(8):118-124 NING Jian. Improvement and application of static security on-line correction strategy based on power grid division identification[J]. Electric Power, 2017, 50(8):118-124