Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Science

  • Mladen Kezunovic
  • Zoran Obradovic
  • Tatjana Dokic
  • Bei Zhang
  • Jelena Stojanovic
  • Payman Dehghanian
  • Po-Chen Chen
Chapter
First Online:
Part of the Studies in Big Data book series (SBD, volume 24)

Abstract

Due to the increase in extreme weather conditions and aging infrastructure deterioration, the number and frequency of electricity network outages is dramatically escalating, mainly due to the high level of exposure of the network components to weather elements. Combined, 75% of power outages are either directly caused by weather-inflicted faults (e.g., lightning, wind impact), or indirectly by equipment failures due to wear and tear combined with weather exposure (e.g. prolonged overheating). In addition, penetration of renewables in electric power systems is on the rise. The country’s solar capacity is estimated to double by the end of 2016. Renewables significant dependence on the weather conditions has resulted in their highly variable and intermittent nature. In order to develop automated approaches for evaluating weather impacts on electric power system, a comprehensive analysis of large amount of data needs to be performed. The problem addressed in this chapter is how such Big Data can be integrated, spatio-temporally correlated, and analyzed in real-time, in order to improve capabilities of modern electricity network in dealing with weather caused emergencies.

Keywords

Aging infrastructure Asset management Big data Data analytics Data mining Insulation coordination Outage management Power system Solar generation forecast Weather impact 
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References

  1. 1.
    L. M. Beard et al., “Key Technical Challenges for the Electric Power Industry and Climate Change,” IEEE Transactions on Energy Conversion, vol. 25, no. 2, pp. 465–473, June 2010.Google Scholar
  2. 2.
    M. Shahidehpour and R. Ferrero, “Time management for assets: chronological strategies for power system asset management,” IEEE Power and Energy Magazine, vol. 3, no. 3, pp. 32–38, May-June 2005.Google Scholar
  3. 3.
    F. Aminifar et al., “Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art,” IEEE Access, vol. 2, pp. 1607–1628, 2014.Google Scholar
  4. 4.
    J. Endrenyi et al., “The present status of maintenance strategies and the impact of maintenance on reliability,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 638–646, Nov 2001.Google Scholar
  5. 5.
    National Oceanic and Atmospheric Administration, [Online] Available: http://www.noaa.gov/ Accessed 12 Feb 2017
  6. 6.
    National Weather Service GIS Data Portal. [Online] Available: http://www.nws.noaa.gov/gis/ Accessed 12 Feb 2017
  7. 7.
    National Digital Forecast Database. [Online] Available: http://www.nws.noaa.gov/ndfd/ Accessed 12 Feb 2017
  8. 8.
    National Weather Service Doppler Radar Images. [Online] Available. http://radar.weather.gov/ Accessed 12 Feb 2017
  9. 9.
    Data Access, National Centers for Environmental Information. [Online] Available: http://www.ncdc.noaa.gov/data-access Accessed 12 Feb 2017
  10. 10.
    Climate Data Online: Web Services Documentation. [Online] Available: https://www.ncdc.noaa.gov/cdo-web/webservices/v2 Accessed 12 Feb 2017
  11. 11.
    National Centers for Environmental Information GIS Map Portal. [Online] Available: http://gis.ncdc.noaa.gov/maps/ Accessed 12 Feb 2017
  12. 12.
    Satellite Imagery Products. [Online] Available: http://www.ospo.noaa.gov/Products/imagery/ Accessed 12 Feb 2017
  13. 13.
    Lightning & Atmospheric Electricity Research. [Online] Available: http://lightning.nsstc.nasa.gov/data/index.html Accessed 12 Feb 2017
  14. 14.
    National Weather Service Organization. [Online] Available: http://www.weather.gov/organization_prv Accessed 12 Feb 2017
  15. 15.
    Commercial Weather Vendor Web Sites Serving The U.S. [Online] Available: http://www.nws.noaa.gov/im/more.htm Accessed 12 Feb 2017
  16. 16.
    U. Finke, et al., “Lightning Detection and Location from Geostationary Satellite Observations,” Institut fur Meteorologie und Klimatologie, University Hannover. [Online] Available: http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=pdf_mtg_em_rep26&RevisionSelectionMethod=LatestReleased&Rendition=Web Accessed 12 Feb 2017
  17. 17.
    K. L. Cummins, et al., “The US National Lightning Detection NetworkTM and applications of cloud-to-ground lightning data by electric power utilities,” IEEE Trans. Electromagn. Compat., vol. 40, no. 4, pp. 465–480, Nov. 1998.Google Scholar
  18. 18.
    Vaisala Inc., “Thunderstorm and Lightning Detection Systems,” [Online] Available: http://www.vaisala.com/en/products/thunderstormandlightningdetectionsystems/Pages/default.aspx Accessed 12 Feb 2017
  19. 19.
    Esri, “ArcGIS Platform,” [Online] Available: http://www.esri.com/software/arcgis Accessed 12 Feb 2017
  20. 20.
    P.-C. Chen, T. Dokic, N. Stoke, D. W. Goldberg, and M. Kezunovic, “Predicting Weather-Associated Impacts in Outage Management Utilizing the GIS Framework,” in Proceeding IEEE/PES Innovative Smart Grid Technologies Conference Latin America (ISGT LATAM), Montevideo, Uruguay, 2015, pp. 417–422.Google Scholar
  21. 21.
    B. Meehan, Modeling Electric Distribution with GIS, Esri Press, 2013.Google Scholar
  22. 22.
    A. von Meier, A. McEachern, “Micro-synchrophasors: a promising new measurement technology for the AC grid,” i4Energy Seminar October 19, 2012.Google Scholar
  23. 23.
    Network Time Foundation, “NTP: The Network Time Protocol,” [Online] Available: http://www.ntp.org/ Accessed 12 Feb 2017
  24. 24.
    IRIG Standard, “IRIG Serial Time Code Formats,” September 2004.Google Scholar
  25. 25.
    IEEE Standards, IEEE 1588-2002, IEEE, 8 November 2002.Google Scholar
  26. 26.
    Q. Yan, T. Dokic, M. Kezunovic, “Predicting Impact of Weather Caused Blackouts on Electricity Customers Based on Risk Assessment,” IEEE Power and Energy Society General Meeting, Boston, MA, July 2016.Google Scholar
  27. 27.
    T. Dokic, P.-C. Chen, M. Kezunovic, “Risk Analysis for Assessment of Vegetation Impact on Outages in Electric Power Systems,” CIGRE US National Committe 2016 Grid of the Future Symposium, Philadelphia, PA, October–November 2016.Google Scholar
  28. 28.
    National Conference of State Legislatures (NCSL), [Online]. http://www.ncsl.org/research/energy/renewable-portfolio-standards.aspx Accessed 12 Feb 2017
  29. 29.
    International Electrotechnical Commission (IEC), “Grid integration of large-capacity Renewable Energy sources and use of large-capacity Electrical Energy Storage”, Oct.1, 2012, [Online]. http://www.iec.ch/whitepaper/pdf/iecWP-gridintegrationlargecapacity-LR-en.pdf Accessed 12 Feb 2017
  30. 30.
    Johan Enslin, “Grid Impacts and Solutions of Renewables at High Penetration Levels”, Oct. 26, 2009, [Online]. http://www.eia.gov/energy_in_brief/images/charts/hydro_&_other_generation-2005-2015-large.jpg Accessed 12 Feb 2017
  31. 31.
    A. H. S. Solberg, T. Taxt, and A. K. Jain, “A Markov random field model for classification of multisource satellite imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 34, no. 1, pp. 100–113, 1996.Google Scholar
  32. 32.
    J. Lafferty, A. McCallum, and F. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” in Proceedings of the 18th International Conference on Machine Learning, 2001, vol. 18, pp. 282–289.Google Scholar
  33. 33.
    M. F. Tappen, C. Liu, E. H. Adelson, and W. T. Freeman, “Learning Gaussian Conditional Random Fields for Low-Level Vision,” 2007 IEEE Conference on Computer Vision and Pattern Recognition, vol. C, no. 14, pp. 1–8, 2007.Google Scholar
  34. 34.
    Sutton, Charles, and Andrew McCallum. “An introduction to conditional random fields for relational learning.” Introduction to statistical relational learning (2006): 93–128.Google Scholar
  35. 35.
    Y. Liu, J. Carbonell, J. Klein-Seetharaman, and V. Gopalakrishnan, “Comparison of probabilistic combination methods for protein secondary structure prediction,” Bioinformatics, vol. 20, no. 17, pp. 3099–3107, 2004.Google Scholar
  36. 36.
    M. Kim and V. Pavlovic, “Discriminative learning for dynamic state prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1847–1861, 2009.Google Scholar
  37. 37.
    T. Qin, T.-Y. Liu, X.-D. Zhang, D.-S. Wang, and H. Li, “Global Ranking Using Continuous Conditional Random Fields,” in Proceedings of NIPS’08, 2008, vol. 21, pp. 1281–1288.Google Scholar
  38. 38.
    T.-minh-tri Do and T. Artieres, “Neural conditional random fields,” in Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2010, vol. 9, pp. 177–184.Google Scholar
  39. 39.
    F. Zhao, J. Peng, and J. Xu, “Fragment-free approach to protein folding using conditional neural fields,” Bioinformatics, vol. 26, no. 12, p. i310-i317, 2010.Google Scholar
  40. 40.
    J. Peng, L. Bo, and J. Xu, “Conditional Neural Fields,” in Advances in Neural Information Processing Systems NIPS’09, 2009, vol. 9, pp. 1–9.Google Scholar
  41. 41.
  42. 42.
    S. Kumar and M. Hebert, “Discriminative Random Fields,” International Journal of Computer Vision, vol. 68, no. 2, pp. 179–201, 2006.Google Scholar
  43. 43.
    G. H. Golub and C. F. Van Loan, Matrix Computations, vol. 10, no. 8. The Johns Hopkins University Press, 1996, p. 48.Google Scholar
  44. 44.
    H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications, vol. 48, no. 1. Chapman & Hall/CRC, 2005, p. 263 p.Google Scholar
  45. 45.
    Ristovski, K., Radosavljevic, V., Vucetic, S., Obradovic, Z., “Continuous Conditional Random Fields for Efficient Regression in Large Fully Connected Graphs,” Proc. The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), Bellevue, Washington, July 2013.Google Scholar
  46. 46.
    Slivka, J., Nikolic, M., Ristovski, K., Radosavljevic, V., Obradovic, Z. “Distributed Gaussian Conditional Random Fields Based Regression for Large Evolving Graphs,” Proc. 14th SIAM Int’l Conf. Data Mining Workshop on Mining Networks and Graphs, Philadelphia, April 2014.Google Scholar
  47. 47.
    Glass, J., Ghalwash, M., Vukicevic, M., Obradovic, Z. “Extending the Modeling Capacity of Gaussian Conditional Random Fields while Learning Faster,” Proc. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ, February 2016.Google Scholar
  48. 48.
    Stojkovic, I., Jelisavcic, V., Milutinovic, V., Obradovic, Z. “Distance Based Modeling of Interactions in Structured Regression,” Proc. 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, July 2016.Google Scholar
  49. 49.
    Polychronopoulou, A, Obradovic, Z. “Structured Regression on Multilayer Networks,” Proc. 16th SIAM Int’l Conf. Data Mining (SDM), Miami, FL, May 2016.Google Scholar
  50. 50.
    Radosavljevic, V., Vucetic, S., Obradovic, Z. “Neural Gaussian Conditional Random Fields,” Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, September, 2014.Google Scholar
  51. 51.
    Han, C, Zhang, S., Ghalwash, M., Vucetic, S, Obradovic, Z. “Joint Learning of Representation and Structure for Sparse Regression on Graphs,” Proc. 16th SIAM Int’l Conf. Data Mining (SDM), Miami, FL, May 2016.Google Scholar
  52. 52.
    Stojanovic, J., Jovanovic, M., Gligorijevic, Dj., Obradovic, Z. “Semi-supervised learning for structured regression on partially observed attributed graphs” Proceedings of the 2015 SIAM International Conference on Data Mining (SDM 2015) Vancouver, Canada, April 30–May 02, 2015.Google Scholar
  53. 53.
    Gligorijevic, Dj, Stojanovic, J., Obradovic, Z.”Uncertainty Propagation in Long-term Structured Regression on Evolving Networks,” Proc. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ, February 2016.Google Scholar
  54. 54.
    R. S. Gorur, et al., “Utilities Share Their Insulator Field Experience,” T&D World Magazine, Apr. 2005, [Online] Available: http://tdworld.com/overhead-transmission/utilities-share-their-insulator-field-experience  Accessed 12 Feb 2017
  55. 55.
    T. Dokic, P. Dehghanian, P.-C. Chen, M. Kezunovic, Z. Medina-Cetina, J. Stojanovic, Z. Obradovic “Risk Assessment of a Transmission Line Insulation Breakdown due to Lightning and Severe Weather,” HICCS – Hawaii International Conference on System Science, Kauai, Hawaii, January 2016.Google Scholar
  56. 56.
    A. R. Hileman, “Insulation Coordination for Power Systems,” CRC Taylor and Francis Group, LLC, 1999.Google Scholar
  57. 57.
    Radosavljevic, V., Obradovic, Z., Vucetic, S. (2010) “Continuous Conditional Random Fields for Regression in Remote Sensing,” Proc. 19th European Conf. on Artificial Intelligence, August, Lisbon, Portugal.Google Scholar
  58. 58.
    P. Dehghanian, et al., “A Comprehensive Scheme for Reliability Centered Maintenance Implementation in Power Distribution Systems- Part I: Methodology”, IEEE Trans. on Power Del., vol.28, no.2, pp. 761–770, April 2013.Google Scholar
  59. 59.
    W. Li, Risk assessment of power systems: models, methods, and applications, John Wiley, New York, 2005.Google Scholar
  60. 60.
    R. Billinton and R. N. Allan, Reliability Evaluation of Engineering Systems: Concepts and Techniques, 2nd ed. New York: Plenum, 1992.Google Scholar
  61. 61.
    B. Zhang, P. Dehghanian, M. Kezunovic, “Spatial-Temporal Solar Power Forecast through Use of Gaussian Conditional Random Fields,” IEEE Power and Energy Society General Meeting, Boston, MA, July 2016.Google Scholar
  62. 62.
    C. Yang, and L. Xie, “A novel ARX-based multi-scale spatio-temporal solar power forecast model,” in 2012 North American Power Symposium, Urbana-Champaign, IL, USA, Sep. 9–11, 2012.Google Scholar
  63. 63.
    California Irrigation Management Information System (CIMIS), [Online]. Available: http://www.cimis.water.ca.gov/ Accessed 12 Feb 2017
  64. 64.
    C. Yang, A. Thatte, and L. Xie, “Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation,” IEEE Trans. Sustainable Energy, vol. 6, no. 1, pp. 104–112, Jan. 2015.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mladen Kezunovic
    • 1
  • Zoran Obradovic
    • 2
  • Tatjana Dokic
    • 1
  • Bei Zhang
    • 1
  • Jelena Stojanovic
    • 2
  • Payman Dehghanian
    • 1
  • Po-Chen Chen
    • 1
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Computer and Information DepartmentTemple UniversityPhiladelphiaUSA

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