WaveLines: towards effective visualization and analysis of stability in power grid simulation

Tianye ZHANG, Qi WANG, Liwen LIN, Jiazhi XIA, Xiwang XU, Yanhao HUANG, Xiaonan LUO, Wenting ZHENG, Wei CHEN

PDF(968 KB)
PDF(968 KB)
Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (6) : 156704. DOI: 10.1007/s11704-019-9393-5
RESEARCH ARTICLE

WaveLines: towards effective visualization and analysis of stability in power grid simulation

Author information +
History +

Abstract

Closely related to the safety and stability of power grids, stability analysis has long been a core topic in the electric industry. Conventional approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive conditions. The lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis, instability reasoning and pattern recognition. To facilitate visual exploration and reasoning on the simulation data, we introduce WaveLines, a visual analysis approach which supports the supervisory control of multivariate simulation time series of power grids. We design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced operators. Experiments have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.

Keywords

stability / visual analysis / power grid / simulation data

Cite this article

Download citation ▾
Tianye ZHANG, Qi WANG, Liwen LIN, Jiazhi XIA, Xiwang XU, Yanhao HUANG, Xiaonan LUO, Wenting ZHENG, Wei CHEN. WaveLines: towards effective visualization and analysis of stability in power grid simulation. Front. Comput. Sci., 2021, 15(6): 156704 https://doi.org/10.1007/s11704-019-9393-5

References

[1]
Steinmetz C P. Power control and stability of electric generating stations. IEEE Transactions of the American Institute of Electrical Engineers, 1920, 39(2): 1215–1287
CrossRef Google scholar
[2]
Vassell G S. The northeast blackout of 1965. Public Utilities Fortnightly (United States), 1990, 126: 8
[3]
Kundur P, Paserba J, Ajjarapu V, Andersson G, Bose A, Canizares C, Hatziargyriou N, Hill D, Stankovic A, Taylor C. Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions. IEEE Transactions on Power Systems, 2004, 19(3): 1387–1401
CrossRef Google scholar
[4]
Glover J D, Sarma M S, Overbye T. Power System Analysis & Design. 5th ed. Cengage Learning, 2012
[5]
Wang G, Tao J Q, Xu X W, Gao D B, Xue J W, Jia W, Liu J Q, Shao G H. 500kv man-made three-phase earthing short circuit experiment in northeast power grid. Power System Technology, 2007, 4: 42
[6]
Zhu F, Tang Y, Zhang D X, Zhang H B, Jiang Y G, Jiang W P, Zhao H G. Influence of excitation and governor model parameters on simulation of large-disturbance test in northeast china power grid. Power System Technology, 2007, 31(4): 69–74
[7]
Tavora C J, Smith O J. Stability analysis of power systems. IEEE Transactions on Power Apparatus and Systems, 1972, 3: 1138–1144
CrossRef Google scholar
[8]
Bergen A R, Hill D J. A structure preserving model for power system stability analysis. IEEE Transactions on Power Apparatus and Systems, 1981, 100(1): 25–35
CrossRef Google scholar
[9]
Vu T L, Turitsyn K. A framework for robust assessment of power grid stability and resiliency. IEEE Transactions on Automatic Control, 2016, 62(3): 1165–1177
CrossRef Google scholar
[10]
Tabari M, Yazdani A. Stability of a dc distribution system for power system integration of plug-in hybrid electric vehicles. IEEE Transactions on Smart Grid, 2014, 5(5): 2564–2573
CrossRef Google scholar
[11]
Zhou F, Lin X, Liu C, Zhao Y, Xu P, Ren L, Xue T, Ren L. A survey of visualization for smart manufacturing. Journal of Visualization, 2019, 22(2): 419–435
CrossRef Google scholar
[12]
Zhou F, Lin X, Luo X, Zhao Y, Chen Y, Chen N, Gui W. Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories. Journal of Visual Languages & Computing, 2018, 44: 58–69
CrossRef Google scholar
[13]
Fang X, Misra S, Xue G, Yang D. Smart grid—the new and improved power grid: a survey. IEEE Communications Surveys & Tutorials, 2012, 14(4): 944–980
CrossRef Google scholar
[14]
Liu R, Li W, Lu Y. Surveys on power system operating state visualization research. Automation of Electric Power Systems, 2004, 28(8): 92–97
[15]
Wong P C, Schneider K, Mackey P, Foote H, Chin J G, Guttromson R, Thomas J. A novel visualization technique for electric power grid analytics. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(3): 410–423
CrossRef Google scholar
[16]
Overbye T J, Weber J D. New methods for the visualization of electric power system information. In: Proceedings of IEEE Symposium on Information Visualization. 2000
[17]
Wong P C, Huang Z, Chen Y, Mackey P, Jin S. Visual analytics for power grid contingency analysis. IEEE Computer Graphics and Applications, 2014, 34(1): 42–51
CrossRef Google scholar
[18]
Flood MD, Lemieux V L, Varga M,Wong BW. The application of visual analytics to financial stability monitoring. Journal of Financial Stability, 2016, 27: 180–197
CrossRef Google scholar
[19]
Alsenaidy M A, Jain N K, Kim J H, Middaugh C R, Volkin D B. Protein comparability assessments and potential applicability of high throughput biophysical methods and data visualization tools to compare physical stability profiles. Frontiers in Pharmacology, 2014, 5: 39
CrossRef Google scholar
[20]
Gröller E. Application of visualization techniques to complex and chaotic dynamical systems. Visualization in Scientific Computing, 1994, 63–71
[21]
Su J, Yu Y, Jia H, Li P, He N, Tang Z, Fu H. Visualization of voltage stability region of bulk power system. In: Proceedings of International Conference on Power System Technology. 2002, 1665–1668
[22]
Vaiman M, Vaiman M, Maslennikov S, Litvinov E, Luo X. Calculation and visualization of power system stability margin based on pmu measurements. In: Proceedings of IEEE International Conference on Smart Grid Communications. 2010, 31–36
CrossRef Google scholar
[23]
Cokkinides G J, Meliopoulos A S, Stefopoulos G, Alaileh R, Mohan A. Visualization and characterization of stability swings via gpssynchronized data. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences. 2007, 120–120
CrossRef Google scholar
[24]
Aigner W, Miksch S, Schumann H, Tominski C. Visualization of Timeoriented Data. Springer Science & Business Media, 2011
CrossRef Google scholar
[25]
Zhao Y, Luo X, Lin X, Wang H, Kui X, Zhou F, Wang J, Chen Y, Chen W. Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1): 590–600
CrossRef Google scholar
[26]
Gotz D, Stavropoulos H. Decisionflow: visual analytics for highdimensional temporal event sequence data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1783–1792
CrossRef Google scholar
[27]
Aigner W, Miksch S, Thurnher B, Biffl S. Planninglines: novel glyphs for representing temporal uncertainties and their evaluation. In: Proceedings of the 9th International Conference on Information Visualisation. 2005, 457–463
[28]
Yang B, Jiang Z, Shangguan J, Li F W, Song C, Guo Y, Xu M. Compressed dynamic mesh sequence for progressive streaming. Computer Animation and Virtual Worlds, 2019, 30(6): e1847
CrossRef Google scholar
[29]
Tominski C, Abello J, Schumann H. Axes-based visualizations with radial layouts. In: Proceedings of the 2004 ACM Symposium on Applied Computing. 2004, 1242–1247
CrossRef Google scholar
[30]
Havre S, Hetzler E, Whitney P, Nowell L. Themeriver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 2002, 8(1): 9–20
CrossRef Google scholar
[31]
Zhao J, Liu Z, Dontcheva M, Hertzmann A, Wilson A. Matrixwave: visual comparison of event sequence data. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015, 259–268
CrossRef Google scholar
[32]
Haroz S, Kosara R, Franconeri S L. The connected scatterplot for presenting paired time series. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(9): 2174–2186
CrossRef Google scholar
[33]
Sedlmair M, Meyer M, Munzner T. Design study methodology: reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(12): 2431–2440
CrossRef Google scholar
[34]
Wang H, Lu Y, Shutters S T, Steptoe M, Wang F, Landis S, Maciejewski R. A visual analytics framework for spatiotemporal trade network analysis. IEEE Transactions on Visualization and Computer Graphics, 2018, 25(1): 331–341
CrossRef Google scholar
[35]
Liu D, Xu P, Ren L. TPFlow: progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis. IEEE Transactions on Visualization and Computer Graphics, 2018, 25(1): 1–11
CrossRef Google scholar
[36]
Moreland K. Diverging color maps for scientific visualization. In: Proceedings of International Symposium on Visual Computing. 2009, 92–103
CrossRef Google scholar
[37]
Moreland K. Why we use bad color maps and what you can do about it. Electronic Imaging, 2016, 2016(16): 1–6
CrossRef Google scholar
[38]
Xu P, Mei H, Ren L, Chen W. ViDX: Visual diagnostics of assembly line performance in smart factories. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(1): 291–300
CrossRef Google scholar

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(968 KB)

Accesses

Citations

Detail

Sections
Recommended

/