Stream Computing and Dynamic Visualization for Electric Power Equipment Monitoring Data

doi:10.3969/j.issn.1000－7229.2017.05.012

Abstract

Abstract: Real-time analysis and visualization of power equipment monitoring data are the important contents of smart grid construction. The traditional data processing model represented by Hadoop cannot meet the requirements of business delay. This paper presents a method of stream computing and dynamic visualization for power equipment monitoring data based on Alibaba Cloud Stream Compute, and uses Stream Compute upstream and downstream service to build an application system for time-frequency analysis and visualization of power equipment monitoring data. The experimental tests show that the overall processing delay of the system is controlled at the second level, which can meet the performance requirements of on-line monitoring and real-time data display.

Key words: on-line monitoring, big data, stream computing, data visualization, Alibaba cloud

CLC Number:

TM 93

LI Li, ZHU Yongli, SONG Yaqi . Stream Computing and Dynamic Visualization for Electric Power Equipment Monitoring Data[J]. Electric Power Construction, 2017, 38(5): 91-.

References

［1］ Tom W. Hadoop权威指南:中文版［M］. 周敏奇，王晓玲，金澈清, 译. 北京: 清华大学出版社, 2010:51-55.

［2］ DEAN J, GHEMAWAT S.MapReduce: simplified data processing on large clusters［C］//6th Conference on Symposium on Opearting Systems Design & Implementation. Berkeley:USENIX Association, 2004:137-150.

［3］ AGNEESWARAN V S. Big data analytics beyond hadoop : real-time applications with storm, spark, and more hadoop alte［M］. New Jersey：Pearson Education, 2014：55-70.

［4］孙大为, 张广艳, 郑纬民. 大数据流式计算:关键技术及系统实例［J］.软件学报, 2014, 25(4):839-862.

SUN Dawei, ZHANG Guangyan, ZHENG Weimin. Big data stream computing: Technologies and instances［J］. Journal of Software, 2014, 25(4):839-862.

［5］林子雨, 林琛, 冯少荣,等. MESHJOIN*:实时数据仓库环境下的数据流更新算法［J］. 计算机科学与探索, 2010, 04(10):927-939.

LIN Ziyu, LIN Chen, FENG Shaorong, et al. MESHJOIN*: An algorithm supporting streaming updates in a real-time data warehouse［J］. Journal of Frontiers of Computer Science and Technology, 2010, 4(10):927-939.

［6］ SILVA B N, KHAN M, HAN K. Big data analytics embedded smart city architecture for performance enhancement through real-time data processing and decision-making［J/OL］.Wireless Communications and Mobile Computing,2017, ［2017-01-18］.https://

DOI.org/10.1155/2017/9429676.

［7］乔通, 赵卓峰, 丁维龙. 面向套牌甄别的流式计算系统［J］. 计算机应用, 2017, 37(1):153-158.

QIAO Tong, ZHAO Zhuofeng, DING Weilong. Stream computing system for monitoring copy plate vehicles［J］. Journal of Computer Applications, 2017, 37(1):153-158.

［8］王德文, 杨力平. 智能电网大数据流式处理方法与状态监测异常检测［J］. 电力系统自动化, 2016, 40(14):122-128.

WANG Dewen, YANG Liping. Stream processing method and condition monitoring anomaly detection for big data in smart grid［J］. Automation of Electric Power Systems, 2016, 40(14): 122-128.

［9］刘子英, 唐宏建, 肖嘉耀,等. 基于流式计算的Web实时故障诊断分析与设计［J］. 华东交通大学学报, 2014(1):119-123.

LIU Ziying, TANG Hongjian, XIAO Jiayao, et al. Analysis and design of web real-time fault diagnosis based on stream computing［J］. Journal of East China Jiaotong University, 2014(1):119-123.

［10］高欢. 基于流式计算的网络舆情分析模型研究［J］. 情报学报, 2016, 35(7):723-729.

GAO Huan. Research on model of network public opinion analysis based on stream computing［J］. Journal of the China Society for Scientific and Technical Information, 2016, 35(7):723-729.

［11］张丽岩, 马健. 流式计算在交通信息实时处理中的应用框架初探［J］. 物流科技, 2014, 37(9):8-9.

ZHANG Liyan, MA Jian. A preliminary application framework study of stream computing in traffic information real-time processing［J］. Logistics Sci-Tech, 2014, 37(9):8-9.

［12］周建宁, 徐晓东, 蔡岗. 流式计算在交通管理中应用研究［J］. 中国公共安全:学术版, 2016(1):70-75.

ZHOU Jianning, XU Xiaodong, CAI Gang. Study on the application of steam computing in traffic management［J］. China Public Security， Academy Edition, 2016(1):70-75.

［13］ SHRUTHI K, SIDDHARTH P. Easy, real-time big data analysis using storm ［EB/OL］. ［2012-12-04］. http://www.drdobbs.com/cloud/easy-real-time-big-data-analysis-using-s/240143874?pgno=1.

［14］张少敏, 孙婕, 王保义. 基于Storm的智能电网广域测量系统数据实时加密［J］. 电力系统自动化, 2016, 40(21):123-127.

ZHANG Shaomin, SUN Jie, WANG Baoyi. Storm based real-time data encryption in wide area measurement system of smart grid［J］. Automation of Electric Power Systems, 2016, 40(21):123-127.

［15］王铭坤, 袁少光, 朱永利,等. 基于Storm的海量数据实时聚类［J］. 计算机应用, 2014, 34(11):3078-3081.

WANG Mingkun, YUAN Shaoguang, ZHU Yongli, et al. Real-time clustering for massive data using storm［J］. Journal of Computer Applications, 2014, 34(11):3078-3081.

［16］阿里云. 流计算产品特点［EB/OL］. ［2017-02-28］. https://help.aliyun.com/document_detail/49930.html ?spm=5176.doc49929.6.550.DVbqvj.

［17］阿里云. 阿里云DataHub［EB/OL］. ［2016-11-21］. https://data.aliyun.com/product/datahub?spm=a2c0j.117599.588239.11.abJECp.

［18］阿里云. DataV数据可视化［EB/OL］. ［2016-09-15］. https://data.aliyun.com/visual/datav?spm=a2c0j.117599.416540.109.abJECp.

［19］鲍永胜. 局部放电脉冲波形特征提取及分类技术［J］. 中国电机工程学报, 2013, 33(28):168-175.

BAO Yongsheng. Partial discharge pulse waveform feature extraction and classification techniques［J］. Proceedings of the CSEE, 2013, 33(28):168-175.

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