Spatial Predictive Modeling of Power Outages Resulting from Distribution Equipment Failure: A Case of Thailand

Authors

  • Thanaporn Thitisawat IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand
  • Supaporn Kiattisin IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand
  • Smitti Darakorn Na Ayuthaya IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.1954

Keywords:

Spatial Predictive Modeling, Geographic Information System (GIS), Power Outages, Reliable Electric Distribution, Electricity Preventive Maintenance, Geospatial Artificial Intelligence, Machine Learning, Spatial Data Analytic

Abstract

This research develops a location-based predictive model for distribution equipment failure for use in preventative maintenance scheduling and planning. This study focuses on equipment-related failures because they are one of the main causes of outages in Thailand. Geographic Information Systems (GIS) data was integrated with asset data to predict the equipment failure of distribution equipment. Data on assets and outages from the Provincial Electricity Authority (PEA) was merged with GIS data from multiple sources, including elevation data, weather data, natural landmarks, and points of interest (POIs). Data was split into four regional datasets, and Random Forests (RF) feature selection and structural equation modeling was used to identify and confirm the most important features in each region. Logistic regression and RF regression were then used to estimate failures. RF regression was more effective than logistic regression at estimating equipment failure. The asset age and electrical load were significant predictors of outages. There were also geographic features that were significant predictors in each region, but which features affected outages varied by region. Thus, the study concluded that the approach developed could be used in preventative maintenance planning with some modification for regional characteristics, including geographic location and patterns of urbanization and industrialization.

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Author Biographies

Thanaporn Thitisawat, IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand

Thanaporn Thitisawat received the bachelor’s degree in business administration (Accounting) from Thammasat University, Thailand in 1993, the master’s degree in business administration (Finance) from Clark University, USA. in 1996, and the second M.Sc. in computer information system with distinction from Bentley University (former Bentley College), USA. in 1997, respectively. She is currently Ph.D. candidate of IT management, faculty of Engineering, Mahidol University, Thailand. Her research interests include applying Geographic Information System (GIS), Geospatial Artificial Intelligence, Location Intelligence, Data Analytic, GIS Applications in Utilities and Spatial Predictive Modeling. She has experience working with IT/Geospatial technology leading companies in Thailand. She has been a guest speaker and mentor for many executive leadership programs such as Digital CEO, Chief of Digital Agro Business, Young Digital CEO, etc.

Supaporn Kiattisin, IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand

Supaporn Kiattisin, Ph.D. received a bachelor’s degree in Computer Engineering from Chiang Mai University, Thailand in 1996, the master’s degree in Electrical Engineering from King Mongkut’s University of Technology Thonburi, Thailand in 1999. She received Ph.D. degree in Electric and Computer Engineering from King Mongkut’s University of Technology Thonburi, Thailand in 2008 under the Royal Golden Jubilee Ph.D. scholarships program. She is currently a head of information technology management program at faculty of engineering, Mahidol University and head of Global Enterprise Management Center. Her research interests include enterprise architecture, data governance, big data, internet of thing, data warehouse and business intelligence. She has been an active member in many organizations such as consultant of sustainable agriculture for Ministry of Agriculture and Cooperatives, consultant for information technology projects for Office of Local Government’s Pawnshop Committee, board of director of Government Enterprise Architecture for Thai government under Ministry of Digital Economy and Society, etc.

Smitti Darakorn Na Ayuthaya, IT Management, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand

Smitti Darakorn Na Ayuthaya, Ph.D. received the bachelor’s degree in economic (honor) from University of Thai Chamber of Commerce, Thailand in 1981, the master’s degree in business administration (Marketing) from Colorado University, USA. in 1985 and the second master’s degree in business administration (Innovation Management) from Ramkhamhaeng University, Thailand in 2008. He received the Ph.D. degree in public administration from University of Northern Philippines, Philippines in 2010 and the second Ph.D. degree in business administration from Lyceum of the Philippines University, Philippines in 2020. He is currently a lecturer with IT Management, faculty of Engineering, Mahidol University, Thailand. His research interests include digital economy, innovative business engineering, economy value management and evaluation and control. He has been an active board member in many organizations such as the Zoological Park Organization of Thailand, the Marketing Organization for Farmers, Ministry of Agriculture and Cooperative, etc.

References

T. Winther, The impact of electricity: Development, desires and dilemmas, Berghahn Books, London, 2008.

S.A. Sarkodie, S. Adams, Electricity access, human development index, governance and income inequality in Sub-Saharan Africa, Energy Reports. 6 (2020) 455–466. https://doi.org/10.1016/j.egyr.2020.02.009.

S.M. Naeem Nawaz, S. Alvi, Energy security for socio-economic and environmental sustainability in Pakistan, Heliyon. 4 (2018) 854. https://doi.org/10.1016/j.heliyon.2018.

S. Adams, F. Atsu, E.M. Klobodu, L. Richmond, Electricity transmission, distribution losses and economic growth in South Africa, Heliyon. 6 (2020). https://doi.org/10.1016/j.heliyon.2020.e05564.

J. Ayaburi, M. Bazilian, J. Kincer, T. Moss, Measuring “Reasonably Reliable” access to electricity services, Electricity Journal. 33 (2020) 106828. https://doi.org/10.1016/j.tej.2020.106828.

P.J. Gertler, K. Lee, A.M. Mobarak, Electricity reliability and economic development in cities: A microeconomic perspective, Oxford, 2017.

World Bank, Income classification and lending groups, World Bank Data. (2020). https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

World Bank, Access to electricity (% of population) – Thailand, World Bank Open Data. (2022). https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?locations=TH.

World Bank, GovData360, Global Governance Practice (GGP). (2022). https://govdata360.worldbank.org/.

J. Arlet, Electricity tariffs, power outages and firm performance: A comparative analysis, in: Proceedings of the DECRG Kuala Lumpur Seminar Series, Kuala Lumpur, Malaysia, 2017.

S. Panya, W. Pattaraprakorn, T. Detmote, P. Teansri, P. Bhasaputra, Economic impact of power outage in Thailand: Industry perspectives, Proceedings of the International Conference on Energy and Sustainable Development: Issues and Strategies, ESD 2010. (2010). https://doi.org/10.1109/esd.2010.5598792.

P. Teansri, R. Bhasaputra, W. Pattaraprakorn, P. Bhasaputra, Outage Cost of Industries in Thailand by Considering Thailand Standard Industrial Classification, GMSARN International Journal. 4 (2010) 37–48.

H. Haes Alhelou, M.E. Hamedani-Golshan, T.C. Njenda, P. Siano, A survey on power system blackout and cascading events: Research motivations and challenges, Energies (Basel). 12 (2019) 1–28. https://doi.org/10.3390/en12040682.

C. Silva, M. Saraee, Electricity distribution network: Seasonality and the dynamics of equipment failures related network faults, 2020 Advances in Science and Engineering Technology International Conferences, ASET 2020. (2020). https://doi.org/10.1109/ASET48392.2020.9118274.

B.A. Wender, M.G. Morgan, K.J. Holmes, Enhancing the Resilience of Electricity Systems, Engineering. 3 (2017) 580–582. https://doi.org/10.1016/J.ENG.2017.05.022.

H.A. Gabbar, Introduction, in: H.A. Gabbar (Ed.), Smart Grid Energy Engineering, Elsevier, 2017.

Y. Kakumoto, Y. Koyamatsu, A. Shiota, Y. Qudaih, Y. Mitani, Application of Geographic Information System to Power Distribution System Analysis, Energy Procedia. 100 (2016) 360–365. https://doi.org/10.1016/j.egypro.2016.10.189.

A. Vinogradov, A. Vinogradova, V. Bolshev, Analysis of the quantity and causes of outages in LV/MV electric grids, CSEE Journal of Power and Energy Systems. 6 (2020) 537–542. https://doi.org/10.17775/CSEEJPES.2019.01920.

R. Billinton, Basic models and methodologies for common mode and dependent transmission outage events, IEEE Power and Energy Society General Meeting. (2012) 1–8. https://doi.org/10.1109/PESGM.2012.6343943.

M. Papic, S. Agarwal, R.N. Allan, R. Billinton, C.J. Dent, S. Ekisheva, D. Gent, K. Jiang, W. Li, J. Mitra, A. Pitto, A. Schneider, C. Singh, V.V. Vadlamudi, M. Varghese, Research on Common-Mode and Dependent (CMD) Outage Events in Power Systems: A Review, IEEE Transactions on Power Systems. 32 (2017) 1528–1536. https://doi.org/10.1109/TPWRS.2016.2588881.

M. Vaiman, P. Hines, J. Jiang, S. Norris, M. Papic, A. Pitto, Y. Wang, G. Zweigle, Mitigation and prevention of cascading outages: Methodologies and practical applications, IEEE Power and Energy Society General Meeting. (2013) 1–5. https://doi.org/10.1109/PESMG.2013.6672795.

R. Murugan, R. Ramasamy, Understanding the power transformer component failures for health index-based maintenance planning in electric utilities, Eng Fail Anal. 96 (2019) 274–288. https://doi.org/10.1016/j.engfailanal.2018.10.011.

T. Dokic, M. Pavlovski, D. Gligorijevic, M. Kezunovic, Z. Obradovic, Spatially aware ensemble-based learning to predict weather-related outages in transmission, Proceedings of the Annual Hawaii International Conference on System Sciences. (2019) 3484–3493. https://doi.org/10.24251/hicss.2019.422.

J. Wu, H. Wang, L. Yao, Z. Kang, Q. Zhang, Comprehensive evaluation of voltage stability based on EW-AHP and Fuzzy-TOPSIS, Heliyon. 5 (2019). https://doi.org/10.1016/j.heliyon.2019.e02410.

S.C. Nwanya, C.A. Mgbemene, C.C. Ezeoke, O.C. Iloeje, Total cost of risk for privatized electric power generation under pipeline vandalism, Heliyon. 4 (2018) 702. https://doi.org/10.1016/j.heliyon.2018.

J. Niu, J. Su, Y. Yang, Y. Cai, H. Liu, Distribution transformer failure rate prediction model based on multi-source information, CMD 2016 - International Conference on Condition Monitoring and Diagnosis. (2016) 944–947. https://doi.org/10.1109/CMD.2016.7757980.

W. Wascom, Y. Xiang, Time-based preventative maintenance policies for circuit breakers with multiple failure types, in: 2021 Annual Reliability and Maintainability Symposium (RAMS), IEEE, 2021. https://doi.org/10.1109/RAMS48097.2021.9605785.

D. Catenazzo, B. Orflynn, M. Walsh, On the use of wireless sensor networks in preventative maintenance for industry 4.0, Proceedings of the International Conference on Sensing Technology, ICST. 2018-December (2019) 256–262. https://doi.org/10.1109/ICSensT.2018.8603669.

P. Kundu, S. Chopra, B.K. Lad, Development of a Risk Based Maintenance strategy to optimize forecast of gas turbine failures, International Journal of Performability Engineering. 11 (2015) 407–416.

K.-T. Chung, Geographic information system, in: D. Richardson, N. Castree, M.E. Goodchild, A. Kobayashi, W. Liu, R.A. Marston (Eds.), The International Encyclopedia of Geography, John Wiley and Sons, 2019: pp. 1–10. https://doi.org/10.1002/9781118786352.wbieg0152.pub2.

K. Zhou, C. Fu, S. Yang, Big data driven smart energy management: From big data to big insights, Renewable and Sustainable Energy Reviews. 56 (2016) 215–225. https://doi.org/10.1016/j.rser.2015.11.050.

P.C. Chen, M. Kezunovic, Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management, IEEE Trans Smart Grid. 7 (2016) 2827–2836. https://doi.org/10.1109/TSG.2016.2576282.

J.B. Leite, J.R.S. Mantovani, T. Dokic, Q. Yan, P.C. Chen, M. Kezunovic, Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics, IEEE Transactions on Power Systems. 34 (2019) 4249–4257. https://doi.org/10.1109/TPWRS.2019.2913090.

R.D. Flamenbaum, T. Pompo, C. Havenstein, J. Thiemsuwan, Machine Learning in Support of Electric Distribution Asset Failure Prediction, SMU Data Science Review. 2 (2019) 16.

V. Sultan, B. Hilton, How may location analytics be used to enhance the reliability of the smart grid?, Inventions. 4 (2019). https://doi.org/10.3390/inventions4030039.

S.J. Rey, D. Arribas-Bel, L.J. Wolf, Geographic data science with PySAL and the PyData Stack, (2020).

S. Chen, X. Zhang, S. Wei, T. Yang, J. Guan, W. Yang, L. Qu, Y. Xu, An energy planning oriented method for analyzing spatial-temporal characteristics of electric loads for heating/cooling in district buildings with a case study of one university campus, Sustain Cities Soc. 51 (2019) 101629. https://doi.org/10.1016/j.scs.2019.101629.

T. Corbin, Learning ArcGIS Pro 2, 2nd ed., Packt, 2020.

B.M. Byrne, Structural equation modeling with AMOS: Basic concepts, applications and programming, 3rd ed., Routledge, London, 2016. https://doi.org/10.4324/9781410600219.

Natural Earth (2022). Natural Earth. https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-ocean.

NASA/USGS/JPL-Caltech (2007). NASA SRTM Digital Elevation 30m. https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003.

Nostra, NOSTRA Map [GIS dataset], (2021).

S. Ozdemir, D. Susaria, Feature engineering made easy, Packt, Birmingham, 2018.

M. Kuhn, K. Johnson, Feature engineering and selection: A practical approach for predictive models, CRC Press, 2020.

N.N.R. Ranga Suri, N. Murty M, G. Athithan, Outlier detection: Techniques and applications. A data mining perspective, Springer, 2019.

T. Jo, Machine learning foundations: Supervised, unsupervised, and advanced learning, Springer, 2021.

M. Kuhn, K. Johnson, Applied predictive modeling, Springer, 2019.

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Published

2023-08-14

How to Cite

Thitisawat, T. ., Kiattisin, S. ., & Ayuthaya, S. D. N. . (2023). Spatial Predictive Modeling of Power Outages Resulting from Distribution Equipment Failure: A Case of Thailand. Journal of Mobile Multimedia, 19(05), 1195–1220. https://doi.org/10.13052/jmm1550-4646.1954

Issue

2023: Vol 19 Iss 5

Section

ECTI

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