Data-driven prediction for the number of distribution network users experiencing typhoon power outages
Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven mode...
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| Veröffentlicht in: | IET generation, transmission & distribution Jg. 14; H. 24; S. 5844 - 5850 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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The Institution of Engineering and Technology
18.12.2020
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven model to predict the number of distribution network users that may experience power outages when a typhoon passes by. To improve the accuracy of the prediction model, twenty six explanatory variables from meteorological factors, geographical factors and power grid factors are considered. In addition, the authors compared the application effect of five different machine learning regression algorithms, including linear regression, support vector regression, classification and regression tree, gradient boosting decision tree and random forest (RF). It turns out that the RF algorithm shows the best performance. The simulation indicates that the accuracy of the optimal model error within ±30% can reach up to 86%. The proposed method can improve the prediction accuracy through continuous learning on the existing basis. The prediction results can provide efficient guidance for emergency preparedness during typhoon disaster, and can be used as a basis to notify the distribution network users who are likely to lose power. |
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| AbstractList | Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven model to predict the number of distribution network users that may experience power outages when a typhoon passes by. To improve the accuracy of the prediction model, twenty six explanatory variables from meteorological factors, geographical factors and power grid factors are considered. In addition, the authors compared the application effect of five different machine learning regression algorithms, including linear regression, support vector regression, classification and regression tree, gradient boosting decision tree and random forest (RF). It turns out that the RF algorithm shows the best performance. The simulation indicates that the accuracy of the optimal model error within ±30% can reach up to 86%. The proposed method can improve the prediction accuracy through continuous learning on the existing basis. The prediction results can provide efficient guidance for emergency preparedness during typhoon disaster, and can be used as a basis to notify the distribution network users who are likely to lose power. |
| Author | Hou, Hui Li, Min Geng, Hao Huang, Yong Yu, Jufang Zhu, Ling Li, Xianqiang |
| Author_xml | – sequence: 1 givenname: Hui surname: Hou fullname: Hou, Hui organization: 1School of Automation, Wuhan University of Technology, Wuhan, People's Republic of China – sequence: 2 givenname: Jufang orcidid: 0000-0002-0473-4998 surname: Yu fullname: Yu, Jufang organization: 1School of Automation, Wuhan University of Technology, Wuhan, People's Republic of China – sequence: 3 givenname: Hao surname: Geng fullname: Geng, Hao organization: 1School of Automation, Wuhan University of Technology, Wuhan, People's Republic of China – sequence: 4 givenname: Ling surname: Zhu fullname: Zhu, Ling organization: 2Guangdong Power Grid Co., Ltd. Guangzhou, People's Republic of China – sequence: 5 givenname: Min surname: Li fullname: Li, Min organization: 2Guangdong Power Grid Co., Ltd. Guangzhou, People's Republic of China – sequence: 6 givenname: Yong surname: Huang fullname: Huang, Yong organization: 3Electric Power Research Institute, Guangdong Power Grid Co., Ltd., Guangzhou, People's Republic of China – sequence: 7 givenname: Xianqiang surname: Li fullname: Li, Xianqiang email: lxq@whut.edu.cn organization: 1School of Automation, Wuhan University of Technology, Wuhan, People's Republic of China |
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| CitedBy_id | crossref_primary_10_1016_j_egyr_2022_08_226 crossref_primary_10_1016_j_engappai_2024_108056 crossref_primary_10_1016_j_apenergy_2024_124903 crossref_primary_10_1155_2021_6682242 |
| Cites_doi | 10.1049/iet-gtd.2019.1733 10.1109/TPWRD.2002.804006 10.1016/j.ress.2011.10.012 10.1049/iet-gtd.2018.6971 10.1109/ACCESS.2014.2365716 10.1049/iet-gtd.2016.0872 10.3390/en12020205 10.1061/(ASCE)1076-0342(2005)11:4(258) 10.1016/j.ress.2007.03.038 10.1109/ACCESS.2018.2881949 10.1049/iet-gtd.2018.6389 10.1007/s11069-015-1908-2 10.1016/j.ijepes.2019.105711 10.1007/s11069-010-9672-9 10.1016/j.tej.2014.05.005 10.1109/TPWRS.2007.907587 |
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| Copyright | The Institution of Engineering and Technology 2020 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | regression tree distribution networks regression analysis linear regression random forests meteorological factors random forest power system reliability typhoon power outages power grids gradient methods geographical factors power grid factors power system planning pattern classification support vector machines distribution network users machine learning regression algorithm disasters data-driven model restoration planning classification power engineering computing gradient boosting decision tree support vector regression decision trees power systems storms data-driven prediction |
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| References | Liu, H.; Davidson, R.A.; Apanasovich, T.V. (C17) 2007; 22 Radmer, D.T.; Kuntz, P.A.; Christie, R.D. (C23) 2002; 17 Mohammad, M.H.; Masood, P. (C7) 2020; 3 Chen, Y.; Wang, S.; Chen, B. (C13) 2018; 42 Zhang, Y.; Chen, C.; Xu, L. (C26) 2011; 39 Hou, H.; Geng, H.; Xiao, X. (C32) 2019; 43 Hou, H.; Yu, S.; Wang, H. (C19) 2019; 12 Liu, H.; Davidson, R.A.; Apanasovich, T.V. (C15) 2008; 93 Xue, Y.; Wu, Y.; Xie, Y. (C18) 2013; 37 Gao, W.; Zhou, R.; Zhao, D. (C4) 2017; 11 Quiring, S.M.; Zhu, L.; Guikema, S.D. (C16) 2011; 58 Masoud, S.K.; Kamran, J.; Meghdad, T.K. (C2) 2019; 13 Xie, Y.; Xue, Y.; Wen, F. (C11) 2013; 37 Liu, H.; Davidson, R.A.; Rosowsky, D.V. (C14) 2005; 11 Sun, C.; Wang, X.; Zheng, Y. (C25) 2019; 13 Zarakas, W.P.; Sergici, S.; Bishop, H. (C1) 2014; 27 Huang, Y.; Wei, R.; Zhou, E. (C8) 2018; 42 Wu, Y.; Xue, Y.; Lu, J. (C12) 2016; 40 Xiong, J.; Lin, H.; Wang, Q. (C3) 2011; 39 Guikema, S.D.; Quiring, S.M. (C22) 2012; 99 Wanik, D.W.; Anagnostou, E.N.; Hartman, B.M. (C27) 2015; 79 Zhang, W.; Sheng, W.; Liu, Y. (C24) 2018; 42 Guikema, S.D.; Nateghi, R.; Quiring, S.M. (C21) 2014; 2 Kavousi-Fard, A.; Wang, M.; Su, W. (C6) 2018; 6 Bao, B.; Cheng, R.; Xiong, X. (C9) 2014; 42 Yin, Z.; Ji, X.; Zhang, Y. (C20) 2020; 14 Yuan, S.; Quiring, S.M.; Zhu, L. (C31) 2020; 117 Liang, J.; Chen, J.; Zhang, X. (C28) 2019; 59 Zhao, H.; Zhang, D.; Huang, S. (C34) 2019; 55 2015; 79 2002; 17 2012 2019; 55 2019; 13 2019; 12 2019; 59 2014; 27 1998 2020; 14 2011; 58 2011; 39 2018; 42 2008; 93 2012; 99 2014; 42 2018; 6 2013; 37 2020; 3 2014; 2 2019; 43 2017; 11 2020; 117 2016; 40 2015 2013 2007; 22 2005; 11 Zhao H. (e_1_2_9_35_2) 2019; 55 e_1_2_9_34_2 Liang J. (e_1_2_9_29_2) 2019; 59 e_1_2_9_11_2 e_1_2_9_32_2 Xie Y. (e_1_2_9_12_2) 2013; 37 Zhang Y. (e_1_2_9_27_2) 2011; 39 Li H. (e_1_2_9_30_2) 2012 Bao B. (e_1_2_9_10_2) 2014; 42 Mohammad M.H. (e_1_2_9_8_2) 2020; 3 Xue Y. (e_1_2_9_19_2) 2013; 37 Chen Y. (e_1_2_9_14_2) 2018; 42 Huang Y. (e_1_2_9_9_2) 2018; 42 Zhang W. (e_1_2_9_25_2) 2018; 42 e_1_2_9_16_2 e_1_2_9_15_2 e_1_2_9_18_2 e_1_2_9_17_2 e_1_2_9_21_2 e_1_2_9_20_2 e_1_2_9_23_2 e_1_2_9_22_2 Xiong J. (e_1_2_9_4_2) 2011; 39 e_1_2_9_7_2 e_1_2_9_6_2 e_1_2_9_5_2 e_1_2_9_3_2 e_1_2_9_2_2 Hou H. (e_1_2_9_33_2) 2019; 43 Vapnik V.N. (e_1_2_9_31_2) 1998 e_1_2_9_24_2 e_1_2_9_26_2 e_1_2_9_28_2 Wu Y. (e_1_2_9_13_2) 2016; 40 |
| References_xml | – volume: 40 start-page: 14 issue: 3 year: 2016 end-page: 20 ident: C12 article-title: Spatiotemporal impact of mountain fire disaster on power grid failure rate publication-title: Power Syst. Autom. – volume: 11 start-page: 258 issue: 4 year: 2005 end-page: 267 ident: C14 article-title: Negative binomial regression of electric power outages in hurricanes publication-title: J. Infrastruct. Syst. – volume: 117 start-page: 1 year: 2020 end-page: 12 ident: C31 article-title: Development of a typhoon power outage model in Guangdong, China publication-title: Electr. Power Energy Syst. – volume: 2 start-page: 1364 year: 2014 end-page: 1373 ident: C21 article-title: Predicting hurricane power outages to support storm response planning publication-title: IEEE Access – volume: 13 start-page: 4888 issue: 21 year: 2019 end-page: 4899 ident: C25 article-title: Early warning system for spatiotemporal prediction of fault events in a power transmission system publication-title: IET Gener. Transm. Distrib. – volume: 42 start-page: 2391 issue: 8 year: 2018 end-page: 2398 ident: C24 article-title: Forecasting method of distribution network fault risk level considering the correlation of weather factors publication-title: Power Grid Technol. – volume: 27 start-page: 31 issue: 5 year: 2014 end-page: 41 ident: C1 article-title: Utility investments in resiliency: balancing benefits with cost in an uncertain environment publication-title: Electr. J. – volume: 93 start-page: 897 issue: 6 year: 2008 end-page: 912 ident: C15 article-title: Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms publication-title: Rel. Eng. Syst. Saf. – volume: 59 start-page: 523 issue: 7 year: 2019 end-page: 529 ident: C28 article-title: Anomaly detection based on one-hot encoding and convolutional neural network publication-title: J. Tsinghua Univ., Nat. Sci. Ed. – volume: 39 start-page: 1 issue: 8 year: 2011 end-page: 5 ident: C26 article-title: Power system reliability original parameter estimation based on fuzzy clustering and similarity publication-title: Power Syst. Prot. Control – volume: 43 start-page: 1948 issue: 6 year: 2019 end-page: 1954 ident: C32 article-title: Forecast and evaluation of power outage areas of distribution network users under typhoon disasters publication-title: Power Grid Technol. – volume: 55 start-page: 186 issue: 8 year: 2019 end-page: 192 ident: C34 article-title: Analysis of the relationship between ground flash density and lightning strike failure in Hainan province based on Pearson correlation coefficient publication-title: High Volt. Electr. Appl. – volume: 12 start-page: 1 issue: 2 year: 2019 end-page: 23 ident: C19 article-title: Risk assessment and its visualization of power tower under typhoon disaster based on machine learning algorithms publication-title: Energies – volume: 37 start-page: 32 issue: 18 year: 2013 end-page: 41 ident: C11 article-title: Spatio-temporal assessment of the impact of ice disaster on transmission line failure rate publication-title: Power Syst. Autom. – volume: 3 start-page: 144 issue: 2 year: 2020 end-page: 152 ident: C7 article-title: Quantifying impacts of automation on resilience of distribution systems publication-title: IET Gener. Transm. Distrib. – volume: 37 start-page: 1 issue: 16 year: 2013 end-page: 9 ident: C18 article-title: The extension of power failure prevention framework to early warning of natural disasters publication-title: Power Syst. Autom. – volume: 22 start-page: 2270 issue: 4 year: 2007 end-page: 2279 ident: C17 article-title: Statistical forecasting of electric power restoration times in hurricanes and ice storms publication-title: IEEE Trans. Power Syst. – volume: 14 start-page: 2450 issue: 13 year: 2020 end-page: 2463 ident: C20 article-title: Data-driven approach for real-time distribution network reconfiguration publication-title: IET Gener. Transm. Distrib. – volume: 42 start-page: 79 issue: 14 year: 2014 end-page: 86 ident: C9 article-title: A warning method for transmission line typhoon risk taking into account micro-topography correction publication-title: Power Syst. Prot. Control – volume: 58 start-page: 365 year: 2011 end-page: 390 ident: C16 article-title: Importance of soil and elevation characteristics for modeling hurricane-induced power outages publication-title: Nat. Hazard – volume: 13 start-page: 3302 issue: 15 year: 2019 end-page: 3310 ident: C2 article-title: Bi-level network reconfiguration model to improve the resilience of distribution systems against extreme weather events publication-title: IET Gener. Transm. Distrib. – volume: 39 start-page: 1248 issue: 8 year: 2011 end-page: 1252 ident: C3 article-title: Research on GIS-based early warning model of wind disaster in regional power grid publication-title: East China Electr. Power – volume: 99 start-page: 178 year: 2012 end-page: 182 ident: C22 article-title: Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data publication-title: Reliablity Eng. Sys. Saf. – volume: 11 start-page: 935 issue: 4 year: 2017 end-page: 942 ident: C4 article-title: Heuristic failure prediction model of transmission line under natural disasters publication-title: IET Gener. Transm. Distrib. – volume: 17 start-page: 1170 issue: 4 year: 2002 end-page: 1175 ident: C23 article-title: Predicting vegetation-related failure rates for overhead distribution feeders publication-title: IEEE Trans. Power Deliv. – volume: 79 start-page: 1359 issue: 2 year: 2015 end-page: 1384 ident: C27 article-title: Storm outage modeling for an electric distribution network in northeastern USA publication-title: Nat. Hazards – volume: 6 start-page: 72311 year: 2018 end-page: 72326 ident: C6 article-title: Stochastic resilient post-hurricane power system recovery based on Mobile emergency resources and reconfigurable networked microgrids publication-title: IEEE Access – volume: 42 start-page: 2295 issue: 7 year: 2018 end-page: 2302 ident: C13 article-title: Failure probability assessment method of transmission channel structure considering geographic elevation information in typhoon environment publication-title: Power Grid Technol. – volume: 42 start-page: 142 issue: 23 year: 2018 end-page: 150 ident: C8 article-title: Early warning method of transmission line damage under typhoon disaster publication-title: Power Syst. Autom. – volume: 13 start-page: 3302 issue: 15 year: 2019 end-page: 3310 article-title: Bi‐level network reconfiguration model to improve the resilience of distribution systems against extreme weather events publication-title: IET Gener. Transm. Distrib. – volume: 3 start-page: 144 issue: 2 year: 2020 end-page: 152 article-title: Quantifying impacts of automation on resilience of distribution systems publication-title: IET Gener. Transm. Distrib. – volume: 13 start-page: 4888 issue: 21 year: 2019 end-page: 4899 article-title: Early warning system for spatiotemporal prediction of fault events in a power transmission system publication-title: IET Gener. Transm. Distrib. – volume: 55 start-page: 186 issue: 8 year: 2019 end-page: 192 article-title: Analysis of the relationship between ground flash density and lightning strike failure in Hainan province based on Pearson correlation coefficient publication-title: High Volt. Electr. Appl. – volume: 17 start-page: 1170 issue: 4 year: 2002 end-page: 1175 article-title: Predicting vegetation‐related failure rates for overhead distribution feeders publication-title: IEEE Trans. Power Deliv. – volume: 117 start-page: 1 year: 2020 end-page: 12 article-title: Development of a typhoon power outage model in Guangdong, China publication-title: Electr. Power Energy Syst. – volume: 14 start-page: 2450 issue: 13 year: 2020 end-page: 2463 article-title: Data‐driven approach for real‐time distribution network reconfiguration publication-title: IET Gener. Transm. Distrib. – volume: 2 start-page: 1364 year: 2014 end-page: 1373 article-title: Predicting hurricane power outages to support storm response planning publication-title: IEEE Access – volume: 59 start-page: 523 issue: 7 year: 2019 end-page: 529 article-title: Anomaly detection based on one‐hot encoding and convolutional neural network publication-title: J. Tsinghua Univ., Nat. Sci. Ed. – volume: 37 start-page: 1 issue: 16 year: 2013 end-page: 9 article-title: The extension of power failure prevention framework to early warning of natural disasters publication-title: Power Syst. Autom. – volume: 6 start-page: 72311 year: 2018 end-page: 72326 article-title: Stochastic resilient post‐hurricane power system recovery based on Mobile emergency resources and reconfigurable networked microgrids publication-title: IEEE Access – volume: 40 start-page: 14 issue: 3 year: 2016 end-page: 20 article-title: Spatiotemporal impact of mountain fire disaster on power grid failure rate publication-title: Power Syst. Autom. – volume: 12 start-page: 1 issue: 2 year: 2019 end-page: 23 article-title: Risk assessment and its visualization of power tower under typhoon disaster based on machine learning algorithms publication-title: Energies – volume: 22 start-page: 2270 issue: 4 year: 2007 end-page: 2279 article-title: Statistical forecasting of electric power restoration times in hurricanes and ice storms publication-title: IEEE Trans. Power Syst. – volume: 79 start-page: 1359 issue: 2 year: 2015 end-page: 1384 article-title: Storm outage modeling for an electric distribution network in northeastern USA publication-title: Nat. Hazards – year: 1998 – year: 2012 – volume: 11 start-page: 935 issue: 4 year: 2017 end-page: 942 article-title: Heuristic failure prediction model of transmission line under natural disasters publication-title: IET Gener. Transm. Distrib. – volume: 42 start-page: 79 issue: 14 year: 2014 end-page: 86 article-title: A warning method for transmission line typhoon risk taking into account micro‐topography correction publication-title: Power Syst. Prot. Control – volume: 37 start-page: 32 issue: 18 year: 2013 end-page: 41 article-title: Spatio‐temporal assessment of the impact of ice disaster on transmission line failure rate publication-title: Power Syst. Autom. – volume: 43 start-page: 1948 issue: 6 year: 2019 end-page: 1954 article-title: Forecast and evaluation of power outage areas of distribution network users under typhoon disasters publication-title: Power Grid Technol. – volume: 42 start-page: 142 issue: 23 year: 2018 end-page: 150 article-title: Early warning method of transmission line damage under typhoon disaster publication-title: Power Syst. Autom. – volume: 11 start-page: 258 issue: 4 year: 2005 end-page: 267 article-title: Negative binomial regression of electric power outages in hurricanes publication-title: J. Infrastruct. Syst. – volume: 93 start-page: 897 issue: 6 year: 2008 end-page: 912 article-title: Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms publication-title: Rel. Eng. Syst. Saf. – volume: 99 start-page: 178 year: 2012 end-page: 182 article-title: Hybrid data mining‐regression for infrastructure risk assessment based on zero‐inflated data publication-title: Reliablity Eng. Sys. Saf. – volume: 42 start-page: 2391 issue: 8 year: 2018 end-page: 2398 article-title: Forecasting method of distribution network fault risk level considering the correlation of weather factors publication-title: Power Grid Technol. – volume: 27 start-page: 31 issue: 5 year: 2014 end-page: 41 article-title: Utility investments in resiliency: balancing benefits with cost in an uncertain environment publication-title: Electr. J. – volume: 58 start-page: 365 year: 2011 end-page: 390 article-title: Importance of soil and elevation characteristics for modeling hurricane‐induced power outages publication-title: Nat. Hazard – volume: 42 start-page: 2295 issue: 7 year: 2018 end-page: 2302 article-title: Failure probability assessment method of transmission channel structure considering geographic elevation information in typhoon environment publication-title: Power Grid Technol. – year: 2015 – volume: 39 start-page: 1 issue: 8 year: 2011 end-page: 5 article-title: Power system reliability original parameter estimation based on fuzzy clustering and similarity publication-title: Power Syst. Prot. Control – volume: 39 start-page: 1248 issue: 8 year: 2011 end-page: 1252 article-title: Research on GIS‐based early warning model of wind disaster in regional power grid publication-title: East China Electr. Power – year: 2013 – volume: 39 start-page: 1 issue: 8 year: 2011 ident: e_1_2_9_27_2 article-title: Power system reliability original parameter estimation based on fuzzy clustering and similarity publication-title: Power Syst. Prot. Control – volume: 42 start-page: 142 issue: 23 year: 2018 ident: e_1_2_9_9_2 article-title: Early warning method of transmission line damage under typhoon disaster publication-title: Power Syst. Autom. – ident: e_1_2_9_21_2 doi: 10.1049/iet-gtd.2019.1733 – ident: e_1_2_9_24_2 doi: 10.1109/TPWRD.2002.804006 – volume: 37 start-page: 32 issue: 18 year: 2013 ident: e_1_2_9_12_2 article-title: Spatio‐temporal assessment of the impact of ice disaster on transmission line failure rate publication-title: Power Syst. Autom. – ident: e_1_2_9_23_2 doi: 10.1016/j.ress.2011.10.012 – ident: e_1_2_9_3_2 doi: 10.1049/iet-gtd.2018.6971 – volume: 39 start-page: 1248 issue: 8 year: 2011 ident: e_1_2_9_4_2 article-title: Research on GIS‐based early warning model of wind disaster in regional power grid publication-title: East China Electr. Power – volume: 43 start-page: 1948 issue: 6 year: 2019 ident: e_1_2_9_33_2 article-title: Forecast and evaluation of power outage areas of distribution network users under typhoon disasters publication-title: Power Grid Technol. – ident: e_1_2_9_22_2 doi: 10.1109/ACCESS.2014.2365716 – ident: e_1_2_9_5_2 doi: 10.1049/iet-gtd.2016.0872 – ident: e_1_2_9_20_2 doi: 10.3390/en12020205 – volume-title: Statistical learning theory year: 1998 ident: e_1_2_9_31_2 – ident: e_1_2_9_34_2 – volume: 59 start-page: 523 issue: 7 year: 2019 ident: e_1_2_9_29_2 article-title: Anomaly detection based on one‐hot encoding and convolutional neural network publication-title: J. Tsinghua Univ., Nat. Sci. Ed. – ident: e_1_2_9_15_2 doi: 10.1061/(ASCE)1076-0342(2005)11:4(258) – volume: 37 start-page: 1 issue: 16 year: 2013 ident: e_1_2_9_19_2 article-title: The extension of power failure prevention framework to early warning of natural disasters publication-title: Power Syst. Autom. – volume: 40 start-page: 14 issue: 3 year: 2016 ident: e_1_2_9_13_2 article-title: Spatiotemporal impact of mountain fire disaster on power grid failure rate publication-title: Power Syst. Autom. – volume-title: Statistical learning method year: 2012 ident: e_1_2_9_30_2 – ident: e_1_2_9_11_2 – ident: e_1_2_9_16_2 doi: 10.1016/j.ress.2007.03.038 – ident: e_1_2_9_6_2 – ident: e_1_2_9_7_2 doi: 10.1109/ACCESS.2018.2881949 – ident: e_1_2_9_26_2 doi: 10.1049/iet-gtd.2018.6389 – ident: e_1_2_9_28_2 doi: 10.1007/s11069-015-1908-2 – ident: e_1_2_9_32_2 doi: 10.1016/j.ijepes.2019.105711 – volume: 42 start-page: 2391 issue: 8 year: 2018 ident: e_1_2_9_25_2 article-title: Forecasting method of distribution network fault risk level considering the correlation of weather factors publication-title: Power Grid Technol. – ident: e_1_2_9_17_2 doi: 10.1007/s11069-010-9672-9 – volume: 55 start-page: 186 issue: 8 year: 2019 ident: e_1_2_9_35_2 article-title: Analysis of the relationship between ground flash density and lightning strike failure in Hainan province based on Pearson correlation coefficient publication-title: High Volt. Electr. Appl. – volume: 42 start-page: 2295 issue: 7 year: 2018 ident: e_1_2_9_14_2 article-title: Failure probability assessment method of transmission channel structure considering geographic elevation information in typhoon environment publication-title: Power Grid Technol. – volume: 3 start-page: 144 issue: 2 year: 2020 ident: e_1_2_9_8_2 article-title: Quantifying impacts of automation on resilience of distribution systems publication-title: IET Gener. Transm. Distrib. – ident: e_1_2_9_2_2 doi: 10.1016/j.tej.2014.05.005 – ident: e_1_2_9_18_2 doi: 10.1109/TPWRS.2007.907587 – volume: 42 start-page: 79 issue: 14 year: 2014 ident: e_1_2_9_10_2 article-title: A warning method for transmission line typhoon risk taking into account micro‐topography correction publication-title: Power Syst. Prot. Control |
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| Snippet | Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the... |
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| SubjectTerms | classification data‐driven model data‐driven prediction decision trees disasters distribution network users distribution networks geographical factors gradient boosting decision tree gradient methods linear regression machine learning regression algorithm meteorological factors pattern classification power engineering computing power grid factors power grids power system planning power system reliability power systems random forest random forests regression analysis regression tree restoration planning Special Issue: Advanced Data-Analytics for Power System Operation, Control and Enhanced Situational Awareness storms support vector machines support vector regression typhoon power outages |
| Title | Data-driven prediction for the number of distribution network users experiencing typhoon power outages |
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