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WaveLines: towards effective visualization and analysis of stability in power grid simulation |
Tianye ZHANG1, Qi WANG1, Liwen LIN1, Jiazhi XIA2, Xiwang XU3, Yanhao HUANG3, Xiaonan LUO4, Wenting ZHENG1( ), Wei CHEN1 |
1. The state key lab of CAD & CG, Zhejiang University, Hangzhou 310058, China 2. School of Computer Science and Engineering, Central South University, Changsha 410083, China 3. The State Key Lab of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Beijing 100192, China 4. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China |
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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.
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Keywords
stability
visual analysis
power grid
simulation data
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Corresponding Author(s):
Wenting ZHENG
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Just Accepted Date: 11 December 2019
Issue Date: 10 May 2021
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1 |
C P Steinmetz. Power control and stability of electric generating stations. IEEE Transactions of the American Institute of Electrical Engineers, 1920, 39(2): 1215–1287
https://doi.org/10.1109/T-AIEE.1920.4765322
|
2 |
G S Vassell. The northeast blackout of 1965. Public Utilities Fortnightly (United States), 1990, 126: 8
|
3 |
P Kundur, J Paserba, V Ajjarapu, G Andersson, A Bose, C Canizares, N Hatziargyriou, D Hill, A Stankovic, C Taylor. 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
https://doi.org/10.1109/TPWRS.2004.825981
|
4 |
J D Glover, M S Sarma, T Overbye. Power System Analysis & Design. 5th ed. Cengage Learning, 2012
|
5 |
G Wang, J Q Tao, X W Xu, D B Gao, J W Xue, W Jia, J Q Liu, G H Shao. 500kv man-made three-phase earthing short circuit experiment in northeast power grid. Power System Technology, 2007, 4: 42
|
6 |
F Zhu, Y Tang, D X Zhang, H B Zhang, Y G Jiang, W P Jiang, H G Zhao. 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 |
C J Tavora, O J Smith. Stability analysis of power systems. IEEE Transactions on Power Apparatus and Systems, 1972, 3: 1138–1144
https://doi.org/10.1109/TPAS.1972.293470
|
8 |
A R Bergen, D J Hill. A structure preserving model for power system stability analysis. IEEE Transactions on Power Apparatus and Systems, 1981, 100(1): 25–35
https://doi.org/10.1109/TPAS.1981.316883
|
9 |
T L Vu, K Turitsyn. A framework for robust assessment of power grid stability and resiliency. IEEE Transactions on Automatic Control, 2016, 62(3): 1165–1177
https://doi.org/10.1109/TAC.2016.2579743
|
10 |
M Tabari, A Yazdani. 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
https://doi.org/10.1109/TSG.2014.2331558
|
11 |
F Zhou, X Lin, C Liu, Y Zhao, P Xu, L Ren, T Xue, L Ren. A survey of visualization for smart manufacturing. Journal of Visualization, 2019, 22(2): 419–435
https://doi.org/10.1007/s12650-018-0530-2
|
12 |
F Zhou, X Lin, X Luo, Y Zhao, Y Chen, N Chen, W Gui. Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories. Journal of Visual Languages & Computing, 2018, 44: 58–69
https://doi.org/10.1016/j.jvlc.2017.11.004
|
13 |
X Fang, S Misra, G Xue, D Yang. Smart grid—the new and improved power grid: a survey. IEEE Communications Surveys & Tutorials, 2012, 14(4): 944–980
https://doi.org/10.1109/SURV.2011.101911.00087
|
14 |
R Liu, W Li, Y Lu. Surveys on power system operating state visualization research. Automation of Electric Power Systems, 2004, 28(8): 92–97
|
15 |
P C Wong, K Schneider, P Mackey, H Foote, J G Chin, R Guttromson, J Thomas. A novel visualization technique for electric power grid analytics. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(3): 410–423
https://doi.org/10.1109/TVCG.2008.197
|
16 |
T J Overbye, J D Weber. New methods for the visualization of electric power system information. In: Proceedings of IEEE Symposium on Information Visualization. 2000
|
17 |
P C Wong, Z Huang, Y Chen, P Mackey, S Jin. Visual analytics for power grid contingency analysis. IEEE Computer Graphics and Applications, 2014, 34(1): 42–51
https://doi.org/10.1109/MCG.2014.17
|
18 |
MD Flood, V L Lemieux, M Varga, BW Wong. The application of visual analytics to financial stability monitoring. Journal of Financial Stability, 2016, 27: 180–197
https://doi.org/10.1016/j.jfs.2016.01.006
|
19 |
M A Alsenaidy, N K Jain, J H Kim, C R Middaugh, D B Volkin. 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
https://doi.org/10.3389/fphar.2014.00039
|
20 |
E Gröller. Application of visualization techniques to complex and chaotic dynamical systems. Visualization in Scientific Computing, 1994, 63–71
|
21 |
J Su, Y Yu, H Jia, P Li, N He, Z Tang, H Fu. Visualization of voltage stability region of bulk power system. In: Proceedings of International Conference on Power System Technology. 2002, 1665–1668
|
22 |
M Vaiman, M Vaiman, S Maslennikov, E Litvinov, X Luo. 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
https://doi.org/10.1109/SMARTGRID.2010.5622011
|
23 |
G J Cokkinides, A S Meliopoulos, G Stefopoulos, R Alaileh, A Mohan. Visualization and characterization of stability swings via gpssynchronized data. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences. 2007, 120–120
https://doi.org/10.1109/HICSS.2007.607
|
24 |
W Aigner, S Miksch, H Schumann, C Tominski. Visualization of Timeoriented Data. Springer Science & Business Media, 2011
https://doi.org/10.1007/978-0-85729-079-3
|
25 |
Y Zhao, X Luo, X Lin, H Wang, X Kui, F Zhou, J Wang, Y Chen, W Chen. Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1): 590–600
https://doi.org/10.1109/TVCG.2019.2934655
|
26 |
D Gotz, H Stavropoulos. Decisionflow: visual analytics for highdimensional temporal event sequence data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1783–1792
https://doi.org/10.1109/TVCG.2014.2346682
|
27 |
W Aigner, S Miksch, B Thurnher, S Biffl. Planninglines: novel glyphs for representing temporal uncertainties and their evaluation. In: Proceedings of the 9th International Conference on Information Visualisation. 2005, 457–463
|
28 |
B Yang, Z Jiang, J Shangguan, F W Li, C Song, Y Guo, M Xu. Compressed dynamic mesh sequence for progressive streaming. Computer Animation and Virtual Worlds, 2019, 30(6): e1847
https://doi.org/10.1002/cav.1847
|
29 |
C Tominski, J Abello, H Schumann. Axes-based visualizations with radial layouts. In: Proceedings of the 2004 ACM Symposium on Applied Computing. 2004, 1242–1247
https://doi.org/10.1145/967900.968153
|
30 |
S Havre, E Hetzler, P Whitney, L Nowell. Themeriver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 2002, 8(1): 9–20
https://doi.org/10.1109/2945.981848
|
31 |
J Zhao, Z Liu, M Dontcheva, A Hertzmann, A Wilson. Matrixwave: visual comparison of event sequence data. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015, 259–268
https://doi.org/10.1145/2702123.2702419
|
32 |
S Haroz, R Kosara, S L Franconeri. The connected scatterplot for presenting paired time series. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(9): 2174–2186
https://doi.org/10.1109/TVCG.2015.2502587
|
33 |
M Sedlmair, M Meyer, T Munzner. Design study methodology: reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(12): 2431–2440
https://doi.org/10.1109/TVCG.2012.213
|
34 |
H Wang, Y Lu, S T Shutters, M Steptoe, F Wang, S Landis, R Maciejewski. A visual analytics framework for spatiotemporal trade network analysis. IEEE Transactions on Visualization and Computer Graphics, 2018, 25(1): 331–341
https://doi.org/10.1109/TVCG.2018.2864844
|
35 |
D Liu, P Xu, L Ren. 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
https://doi.org/10.1109/TVCG.2018.2865018
|
36 |
K Moreland. Diverging color maps for scientific visualization. In: Proceedings of International Symposium on Visual Computing. 2009, 92–103
https://doi.org/10.1007/978-3-642-10520-3_9
|
37 |
K Moreland. Why we use bad color maps and what you can do about it. Electronic Imaging, 2016, 2016(16): 1–6
https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-133
|
38 |
P Xu, H Mei, L Ren, W Chen. ViDX: Visual diagnostics of assembly line performance in smart factories. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(1): 291–300
https://doi.org/10.1109/TVCG.2016.2598664
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