基于工况划分的大规模电厂机组控制数据可视化探索
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摘要
对电厂控制过程中的历史数据进行有效展示与探索,能帮助用户快速深入理解机组的运行状况.由于历史数据涉及时间跨度长,具有多尺度和高密度的特点,并且包含高维多元的复杂参数集合,为可视化设计带来了很大挑战.从机组运行工况和参数相关性角度研究时序数据空间和高维参数空间的集成可视化映射方法,设计了多角度概览视图和多分辨率层次化工况视图用于导航机组的整体运行状态,有效地支持多层次运行工况的展示与探索;然后,设计了高维多元参数分层导航视图,实现了机组参数的灵活筛选和过滤,并与工况视图联动支持用户对不同时段和不同系统层级的参数特征进行探索.基于上述方法,开发了可视化工具iDCS,并将其应用于实际机组控制数据的可视化与分析中,验证了该系统的有效性和适用性.
The effective demonstration and exploration of the historical data from power plant's control process can help users to understand the operating condition of power units quickly and deeply. Since historical data has the characteristics of long time span, multi-scale and high density, and contains complex, high-dimensional and multivariate parameter sets simultaneously, it has brought great challenges to visual design. In this paper, the integrated visual mapping for time-series data space and high-dimensional parameter space is studied from the perspective of power unit operating conditions and parameter correlation. First, a multi-facet overview is de-signed to navigate the global operating state of the power unit, followed by a multi-resolution operating condition view, which can effectively support the display and exploration of multi-level operating conditions. Then, to realize the flexible selection and filtration of parameters of the power unit, a parameter navigation view is designed to support the hierarchical exploration of multivariate parameters. It can be also well-coordinated with the operating condition view to support users to explore the parameter characteristics of different time periods and different system levels. Based on the above methods, a visualization tool, called iDCS, is designed and applied to the visualization and analysis of the real power unit control data, and the validity and applicability of the system are verified.
The effective demonstration and exploration of the historical data from power plant's control process can help users to understand the operating condition of power units quickly and deeply. Since historical data has the characteristics of long time span, multi-scale and high density, and contains complex, high-dimensional and multivariate parameter sets simultaneously, it has brought great challenges to visual design. In this paper, the integrated visual mapping for time-series data space and high-dimensional parameter space is studied from the perspective of power unit operating conditions and parameter correlation. First, a multi-facet overview is de-signed to navigate the global operating state of the power unit, followed by a multi-resolution operating condition view, which can effectively support the display and exploration of multi-level operating conditions. Then, to realize the flexible selection and filtration of parameters of the power unit, a parameter navigation view is designed to support the hierarchical exploration of multivariate parameters. It can be also well-coordinated with the operating condition view to support users to explore the parameter characteristics of different time periods and different system levels. Based on the above methods, a visualization tool, called iDCS, is designed and applied to the visualization and analysis of the real power unit control data, and the validity and applicability of the system are verified.
引文
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[24]Martin A R,Ward M O.High dimensional brushing for interactive exploration of multivariate data[C]//Proceedings of the 6th Conference on Visualization.Los Alamitos:IEEE Computer Society Press,1995:271-278
[25]Siirtola H,R?ih?K J.Interacting with parallel coordinates[J].Interacting with Computers,2006,18(6):1278-1309
[26]Liang Jiye,Feng Chenjiao,Song Peng.A survey on correlation analysis of big data[J].Chinese Journal of Computer,2016,39(1):1-18(in Chinese)(梁吉业,冯晨娇,宋鹏.大数据相关分析综述[J].计算机学报,2016,39(1):1-18)
[2]Aigner W,Miksch S,Schumann H,et al.Visualization of time-oriented data[M].Berlin:Springer,2011
[3]Zhai Shaolei,Huang Xiaobin,Liu Jizhen.Data mining to economic norms of power plant based on condition division[J].Electric Power,2009,42(7):68-71(in Chinese)(翟少磊,黄孝彬,刘吉臻.基于工况划分的电厂经济性指标挖掘[J].中国电力,2009,42(7):68-71)
[4]Overbye T J,Weber J D.Visualization of power system data[C]//Proceedings of Hawaii International Conference on System Sciences.Los Alamitos:IEEE Computer Society Press,2000:1228-1234
[5]Lin J,Keogh E,Lonardi S,et al.Visually mining and monitoring massive time series[C]//Proceedings of International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2004:460-469
[6]Shen Guohui,She Dongxiang,Sun Pai,et al.Research and application of power system visualization technology[J].Power System Technology,2009,33(17):31-36(in Chinese)(沈国辉,佘东香,孙湃,等.电力系统可视化技术研究及应用[J].电网技术,2009,33(17):31-36)
[7]Liu Junyong,Shen Xiaodong,Tian Lifeng,et al.Prospects of visualization under smart grid[J].Electric Power Automation Equipment,2010,30(1):7-13(in Chinese)(刘俊勇,沈晓东,田立峰,等.智能电网下可视化技术的展望[J].电力自动化设备,2010,30(1):7-13)
[8]Li Jing.Real time data collection and analysis for thermal power plant[D].Nanjing:Southeast University,2015(in Chinese)(李敬.火力发电厂实时数据采集与分析[D].南京:东南大学,2015)
[9]Aigner W,Miksch S,Müller W,et al.Visual methods for analyzing time-oriented data[J].IEEE Transactions on Visualization and Computer Graphics,2007,14(1):47-60
[10]Ren Lei,Du Yi,Ma Shuai,et al.Visual analytics towards big data[J].Journal of Software,2014,25(9):1909-1936(in Chinese)(任磊,杜一,马帅,等.大数据可视分析综述[J].软件学报,2014,25(9):1909-1936)
[11]Liu Z,Jiang B,Heer J.imMens:Real-time visual querying of big data[J].Computer Graphics Forum,2013,32(3):421-430
[12]Chen Wei,Shen Zeqian,Tao Yubo.Data visualization[M].Beijing:Publishing House of Electronics Industry,2013(in Chinese)(陈为,沈则潜,陶煜波.数据可视化[M].北京:电子工业出版社,2013)
[13]Stein K,Wegener R,Schlieder C.Pixel-oriented visualization of change in social networks[C]//Proceedings of International Conference on Advances in Social Networks Analysis and Mining.Los Alamitos:IEEE Computer Society Press,2010:233-240
[14]Wijk J J V,Selow E R V.Cluster and calendar based visualization of time series data[C]//Proceedings of the IEEE Symposium on Information Visualization.Los Alamitos:IEEE Computer Society Press,1999:4-9
[15]Ming H,Umeshwar D,Daniel K.Multi-resolution techniques for visual exploration of large time-series data[J].Canadian Journal of Animal Science,2007,80(3):427-434
[16]Mc Lachlan P,Munzner T,Koutsofios E,et al.LiveRAC:interactive visual exploration of system management time-series data[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.New York:ACM Press,2008:1483-1492
[17]Yang J,Ward M O,Rundensteiner E A.InterRing:An anteractive tool for visually navigating and manipulating hierarchical structures[C]//Proceedings of the IEEE Symposium on Information Visualization.Los Alamitos:IEEE Computer Society Press,2002:77-84
[18]Holten D.Hierarchical edge bundles:Visualization of adjacency relations in hierarchical data[J].IEEE Transactions on Visualization and Computer Graphics,2006,12(5):741-748
[19]Chen Yi,Zhen Yuangang,Hu Haiyun,et al.Visualization technique for multi-attrbute in hierarchical structure[J].Journal of Software,2016,27(5):1091-1102(in Chinese)(陈谊,甄远刚,胡海云,等.一种层次结构中多维属性的可视化方法[J].软件学报,2016,27(5):1091-1102)
[20]Tatu A,Albuquerque G,Eisemann M,et al.Automated analytical methods to support visual exploration of high-dimensional data[J].IEEE Transactions on Visualization and Computer Graphics,2011,17(5):584-597
[21]Yuan X,Guo P,Xiao H,et al.Scattering points in parallel coordinates[J].IEEE Transactions on Visualization and Computer Graphics,2009,15(6):1001-1008
[22]Fua Y H,Ward M O,Rundensteiner E A.Hierarchical parallel coordinates for exploration of large datasets[C]//Proceedings of the IEEE Conference on Visualization.Los Alamitos:IEEEComputer Society Press,1999:43-50
[23]Yang J,Peng W,Ward M O,et al.Interactive hierarchical dimension ordering,spacing and filtering for exploration of high dimensional datasets[C]//Proceedings of the IEEE Symposium on Information Visualization.Los Alamitos:IEEE Computer Society Press,2003:105-112
[24]Martin A R,Ward M O.High dimensional brushing for interactive exploration of multivariate data[C]//Proceedings of the 6th Conference on Visualization.Los Alamitos:IEEE Computer Society Press,1995:271-278
[25]Siirtola H,R?ih?K J.Interacting with parallel coordinates[J].Interacting with Computers,2006,18(6):1278-1309
[26]Liang Jiye,Feng Chenjiao,Song Peng.A survey on correlation analysis of big data[J].Chinese Journal of Computer,2016,39(1):1-18(in Chinese)(梁吉业,冯晨娇,宋鹏.大数据相关分析综述[J].计算机学报,2016,39(1):1-18)