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A survey on the development status and application prospects of knowledge graph in smart grids

Wang, Jian, Wang, Xi, Ma, Chaoqun, Kou, Lei·2021·IET Generation, Transmission & Distribution

Abstract

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge‐driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision‐making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

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Research works printed but not linked: 183

  1. 10.1016/j.rser.2017.01.064 · DOI
  2. 10.1109/JPROC.2011.2116752 · DOI
  3. 10.1109/TSG.2013.2265556 · DOI
  4. 10.1109/TSG.2014.2344659 · DOI
  5. 10.1109/TPWRS.2019.2897948 · DOI
  6. Saha B., 2014, Data quality: The other face of big data, IEEE 30th Int. Conf. Data Eng., 1, 1294
  7. 10.1145/1891879.1891881 · DOI · Paper page
  8. 10.1016/j.rser.2018.03.088 · DOI
  9. 10.1007/978-3-642-41335-3_34 · DOI
  10. Tang Y. et al.: Enhancement of power equipment management using knowledge graph. IEEE PES Innov. Smart Grid Technol. Asia905–910(2019) · DOI
  11. 10.1109/TKDE.2017.2754499 · DOI · Paper page
  12. 10.1007/s11227-018-2672-3 · DOI
  13. 10.1016/j.artmed.2017.04.001 · DOI
  14. 10.1001/jama.2013.393 · DOI
  15. 10.1109/TKDE.2015.2426696 · DOI
  16. Bollacker K. et al.: Freebase: A collaboratively created graph database for structuring human knowledge. Proc. ACM SIGMOD Int. Conf. Manag. Data1247–1249(2008) · DOI · Paper page
  17. Auer S., 2007, DBpedia: A nucleus for a web of open data, SWJ, 4825, 722
  18. Suchanek F.M. et al.: Yago: A core of semantic knowledge. 16th Int. World Wide Web Conf. (WWW2007)697–706(2007) · DOI · Paper page
  19. Carlson A. et al.: Toward an architecture for never‐ending language learning. Proc. Natl. Conf. Artif. Intell.1306–1313(2010) · DOI · Paper page
  20. 10.1145/2629489 · DOI
  21. 10.1109/TMM.2017.2724843 · DOI
  22. 10.1016/j.jnca.2016.10.003 · DOI
  23. 10.3233/SW-160218 · DOI · Paper page
  24. 10.1016/j.knosys.2018.03.022 · DOI · Paper page
  25. Chun S. et al.: Knowledge graph modeling for semantic integration of energy services. 2018 IEEE Int. Conf. Big Data Smart Comput. (BigComp)732–735(2018). · DOI
  26. Yun F. et al.: Research on intelligent fault diagnosis of power acquisition based on knowledge graph. 2019 3rd Int. Conf. Electron. Inf. Technol. Comput. Eng. (EITCE)1737–1740(2019) · DOI
  27. 10.1109/5254.920602 · DOI
  28. 10.1109/TCE.2011.5955175 · DOI
  29. 10.1109/TSG.2011.2173508 · DOI
  30. Feng J.Q. et al.: Condition assessment of power system apparatuses using ontology systems. Proc. IEEE Power Eng. Soc. Transm. Distrib. Conf.1–6(2005) · DOI
  31. Liao Z. Liu S.: Substation alarm information processing based on ontology theory. Proc. 5th IEEE Int. Conf. Electr. Util. Deregulation Restruct. Power Technol. (DRPT)2377–2382(2015) · DOI
  32. 10.17775/CSEEJPES.2017.0026 · DOI
  33. Gaha M. et al.: An ontology‐based reasoning approach for electric power Utilities. Proc. 7th Int. Conf. Web Reasoning Rule Syst.95–108(2013) · DOI
  34. 10.17775/CSEEJPES.2015.00003 · DOI
  35. 10.1109/TII.2018.2835567 · DOI
  36. 10.1109/TII.2018.2794982 · DOI
  37. 10.1109/ACCESS.2019.2933859 · DOI
  38. 10.1109/JSYST.2016.2535719 · DOI
  39. 10.1109/TII.2017.2655047 · DOI
  40. 10.1109/ACCESS.2018.2877716 · DOI
  41. 10.1109/ACCESS.2019.2908032 · DOI
  42. 10.1016/j.solener.2017.11.023 · DOI
  43. Sun S. et al.: Power fault preplan text information extraction based on NLP. Proc. 2018 IEEE Int. Conf. Saf. Prod. Informatiz. (IICSPI 2018)617–621(2019) · DOI
  44. Schuddebeurs J.D. et al.: Text data mining as a tool for the asset management decision support process. IET Conf. Publ.1–6(2016) · DOI
  45. Xie C. et al.: A new condition assessment method for distribution transformers based on operation data and record text mining technique. China Int. Conf. Electr. Distrib. (CICED) 1–7(2016) · DOI
  46. Ravi N.N. et al.: Substation transformer failure analysis through text mining. 2019 IEEE 9th Symp. Comput. Appl. Ind. Electron. (ISCAIE)293–298(2019) · DOI
  47. Sheng Y. et al.: Research on multi‐algorithm‐based hierarchical recognition of power customer appeals. 2019 IEEE 4th Int. Conf. Cloud Comput. Big Data Anal. (ICCCBDA)138–143(2019) · DOI
  48. 10.1109/TSMCA.2012.2196431 · DOI
  49. 10.1145/2602574 · DOI
  50. Ritter A. et al.: Open domain event extraction from twitter. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.1104–1112(2012) · DOI
  51. Sakaki T. et al.: Earthquake shakes Twitter users: Real‐time event detection by social sensors. 19th Int. World Wide Web Conf.851–860(2010) · DOI
  52. 10.1109/TSG.2016.2546181 · DOI
  53. 10.1080/17538947.2018.1535000 · DOI
  54. 10.1145/3052931 · DOI
  55. James P., 1992, Knowledge graphs, Order, 501, 6439
  56. 10.1109/TDSC.2017.2697854 · DOI
  57. Hogan A. et al.: Knowledge graphs. arXiv:2003.02320 (2020)https://arxiv.org/abs/2003.02320v3 · arXiv
  58. Zhang Z. et al.: Deep learning on graphs: A survey. IEEE Trans. Knowl. Data Eng. 1 (2020) · DOI
  59. Dong X. et al.: Knowledge vault: A web‐scale approach to probabilistic knowledge fusion. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.601–610(2014) · DOI · Paper page
  60. 10.1007/s11704-016-5228-9 · DOI
  61. Jiang T. et al.: Biomedical knowledge graphs construction from conditional statements. IEEE/ACM Trans. Comput. Biol. Bioinforma. 1 (2020)
  62. 10.1016/j.cageo.2017.12.007 · DOI
  63. 10.1109/JPROC.2015.2483592 · DOI
  64. 10.1109/TKDE.2018.2807452 · DOI · Paper page
  65. 10.1016/j.inffus.2020.03.014 · DOI
  66. 10.1088/1757-899X/569/5/052103 · DOI
  67. 10.1109/TSTE.2016.2623631 · DOI
  68. 10.1109/TIFS.2020.2997134 · DOI
  69. 10.1109/TSMCC.2012.2227472 · DOI
  70. 10.1109/TSE.2014.2327044 · DOI
  71. 10.1109/TBCAS.2015.2483618 · DOI
  72. Wu Y., 2020, Person re‐identification by multi‐scale feature representation learning with random batch feature mask, IEEE Trans. Cogn. Dev. Syst., 1, 2590
  73. 10.1109/TNNLS.2020.2984267 · DOI
  74. 10.1109/TNNLS.2014.2308519 · DOI
  75. 10.1109/TNNLS.2018.2886017 · DOI · Paper page
  76. 10.1109/TC.2014.2345385 · DOI
  77. 10.1109/TSMC.2017.2759026 · DOI
  78. 10.1109/JIOT.2019.2959063 · DOI
  79. 10.1109/TKDE.2018.2807442 · DOI
  80. 10.1109/TNNLS.2017.2673243 · DOI
  81. Xiao H., 2019, Knowledge graph embedding based on multi‐view clustering framework, IEEE Trans. Knowl. Data Eng., 31, 1984
  82. 10.1109/TKDE.2019.2893920 · DOI
  83. 10.1109/TKDE.2016.2610428 · DOI
  84. 10.1109/TKDE.2013.2297918 · DOI
  85. 10.1109/TKDE.2017.2743075 · DOI
  86. 10.1109/TKDE.2018.2866863 · DOI
  87. 10.1016/j.ins.2019.08.051 · DOI
  88. 10.1016/j.ipm.2020.102324 · DOI
  89. 10.1109/TII.2016.2645606 · DOI
  90. 10.1016/j.rser.2015.12.114 · DOI
  91. 10.1016/j.eij.2015.11.004 · DOI
  92. 10.1109/TPAMI.2011.108 · DOI
  93. 10.1049/iet-sen.2016.0189 · DOI
  94. Wang H., 2018, Retrieval method for defect records of power equipment based on knowledge graph technology, Autom. Electr. Power Syst., 42, 158
  95. 10.1016/j.eswa.2019.112948 · DOI · Paper page
  96. Qiao J. et al.: Framework and key technologies of knowledge‐graph‐based fault handling system in power grid. Proc. CSEE40(18) 5837–5849(2020)
  97. Mondal J. Deshpande A.: Managing large dynamic graphs efficiently. Proc. ACM SIGMOD Int. Conf. Manag. Data145–156(2012) · DOI
  98. 10.1016/j.jpdc.2016.07.006 · DOI
  99. 10.1109/TII.2019.2942353 · DOI
  100. 10.1109/TSG.2018.2834219 · DOI · Paper page
  101. 10.1109/TII.2018.2804669 · DOI
  102. 10.1109/TSG.2017.2703842 · DOI
  103. 10.1109/ACCESS.2019.2915987 · DOI
  104. Yang Y. et al.: Public cultural knowledge graph platform. 2017 IEEE 11th Int. Conf. Semant. Comput. (ICSC)322–327(2017) · DOI
  105. Miller J.J.: Graph database applications and concepts with Neo4j. Proc. South. Assoc. Inf. Syst. Conf. Atlanta GA 2324 36 (2013)
  106. 10.1109/TC.2017.2669964 · DOI
  107. Liao H. et al.: Multi‐dimensional index on Hadoop distributed file system. 2010 IEEE Fifth Int. Conf. Networking Archit. Storage240–249(2010) · DOI
  108. 10.1007/s11227-015-1411-2 · DOI
  109. Xin R.S. et al.: Shark: SQL and rich analytics at scale. Proc. ACM SIGMOD Int. Conf. Manag. Data13–24(2013) · DOI
  110. Zhang F. et al.: Collaborative knowledge base embedding for recommender systems. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.353–362(2016) · DOI
  111. 10.1109/TASLP.2018.2864648 · DOI
  112. Yih W.T. et al.: Semantic parsing via staged query graph generation: question answering with knowledge base. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Asian Fed. Nat. Lang. Process. (ACL‐IJCNLP 2015) 1321–13311(2015) · DOI
  113. Marino K. et al.: The more you know: Using knowledge graphs for image classification. Proc. 30th IEEE Conf. Comput. Vis. Pattern Recognition (CVPR 2017) 20–28(2017) · DOI
  114. 10.1038/s41598-017-05778-z · DOI · Paper page
  115. Seifert S. et al.: Combined semantic and similarity search in medical image databases. Med. Imaging 2011 Adv. PACS‐Based Imaging Informatics Ther. Appl.7967796703 (2011) · DOI
  116. 10.1016/j.jbi.2011.01.004 · DOI
  117. 10.1109/TPWRS.2017.2704524 · DOI
  118. 10.1109/TCST.2016.2563385 · DOI
  119. 10.1109/TSG.2018.2810819 · DOI
  120. Liu J., 2015, Research on quality inspection sampling algorithm of electric 95598 customer Service, Power Syst. Technol., 39, 3163
  121. Lindén M. et al.: Categorisation of electricity customers based upon their demand patterns. CIRED ‐ Open Access Proc. J.2017(1) 2628–2631(2017) · DOI
  122. 10.1109/TPWRS.2011.2167524 · DOI
  123. Wang J.Y. et al.: A telecom‐domain online customer service assistant based on question answering with word embedding and intent classification. Proc. IJCNLP 2017 Syst. Demonstr.17–20(2017)
  124. 10.1109/TKDE.2018.2815544 · DOI
  125. 10.1109/TPDS.2017.2665478 · DOI
  126. 10.1109/TKDE.2014.2349924 · DOI
  127. 10.1109/TPDS.2009.97 · DOI
  128. 10.1109/TII.2018.2834619 · DOI
  129. Ghorai C. et al.: A SPEA‐based multimetric routing protocol for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst.1–11(2020) · DOI · Paper page
  130. 10.1109/TITS.2018.2867177 · DOI
  131. 10.1109/TKDE.2017.2766634 · DOI
  132. 10.1109/TASLP.2019.2926125 · DOI
  133. 10.1109/TPAMI.2017.2708709 · DOI
  134. 10.1109/TASLP.2016.2544661 · DOI
  135. Tan G., 2020, HDKG: Hybrid domain features knowledge graph smart question answering system, Comput. Eng. Appl., 56, 232
  136. 10.1109/TWC.2015.2512274 · DOI
  137. 10.1109/TMC.2017.2766623 · DOI
  138. 10.1109/TPWRS.2009.2037006 · DOI
  139. 10.1109/TII.2017.2696534 · DOI
  140. 10.1016/j.apenergy.2015.05.023 · DOI
  141. 10.1016/j.energy.2016.11.125 · DOI
  142. 10.1016/j.energy.2018.07.200 · DOI
  143. 10.1109/TSG.2014.2386780 · DOI
  144. 10.1109/TSG.2014.2357253 · DOI
  145. 10.1109/TSTE.2015.2455552 · DOI
  146. 10.1109/TSTE.2019.2918269 · DOI
  147. 10.1109/TSG.2014.2377178 · DOI
  148. 10.1109/TSG.2019.2962968 · DOI
  149. 10.1109/TSG.2015.2444813 · DOI
  150. 10.1109/TPWRS.2014.2298463 · DOI
  151. 10.1109/TIA.2016.2518995 · DOI
  152. 10.1109/TSG.2015.2444813 · DOI
  153. 10.1109/TSG.2013.2282300 · DOI
  154. 10.1109/TIA.2014.2372043 · DOI
  155. Sun H., 2020, Automatic operator (AO) for decision‐making in dispatch: Research and applications, Power Syst. Technol., 44, 1
  156. Li X., 2019, Construction and application of knowledge graph of power dispatching automation system, Electr. Power, 52, 70
  157. Shan X., 2018, Analysis of key technologies for artificial intelligence applied to power grid dispatch and control, Autom. Electr. Power Syst., 43, 69
  158. Chai B. et al.: Research on applications of artificial intelligence in business management of power grid enterprises. 2019 IEEE 4th Adv. Inf. Technol. Electron. Autom. Control Conf. (IAEAC)683–688(2019) · DOI
  159. 10.1016/j.isatra.2020.01.023 · DOI
  160. 10.1109/TSG.2015.2388778 · DOI
  161. Zhang P. et al.: Short term trend forecast of on‐line monitoring data of dissolved gas in power transformer. 2018 IEEE Electr. Insul. Conf. (EIC) 240–243(2018) · DOI
  162. 10.1016/j.epsr.2017.10.010 · DOI
  163. 10.1109/TSG.2016.2562123 · DOI
  164. 10.1109/TDEI.2018.006630 · DOI
  165. 10.1007/s13042-018-0812-y · DOI
  166. Zhang S., 2018, Predicting model of transformer DGA time series based on WNN‐GNN‐SVM combined algorithm, Electr. Power Autom. Equip., 38, 155
  167. 10.1109/TPWRD.2018.2881747 · DOI
  168. 10.1631/FITEE.1800260 · DOI
  169. Cui B.: Electric device abnormal detection based on IoT and knowledge graph. 2019 IEEE Int. Conf. Energy Internet (ICEI)217–220(2019) · DOI
  170. Su Z. et al.: Automatic knowledge graph construction based on relational data of power terminal equipment. 2020 5th Int. Conf. Comput. Commun. Syst. (ICCCS)761–765(2020) · DOI
  171. Gao Z., 2020, Low voltage distribution network topology verification method based on knowledge graph, Power Syst. Prot. Control, 48, 34
  172. Yun F. et al.: Research on intelligent fault diagnosis of power acquisition based on knowledge graph. 2019 3rd Int. Conf. Electron. Inf. Technol. Comput. Eng. (EITCE) 2019 1737–1740(2019) · DOI
  173. 10.1016/j.tej.2015.02.006 · DOI
  174. 10.1049/iet-stg.2018.0261 · DOI
  175. 10.1109/ACCESS.2019.2933370 · DOI
  176. 10.14778/2350229.2350263 · DOI
  177. 10.14778/2732977.2732982 · DOI
  178. 10.1007/s00778-013-0324-z · DOI
  179. 10.1007/s11704-016-5228-9 · DOI
  180. Wang Z. et al.: Cross‐lingual knowledge linking across wiki knowledge bases. Proc. 21st Annu. Conf. World Wide Web459–468(2012) · DOI
  181. Deshpande O. et al.: Building maintaining and using knowledge bases: A report from the trenches. Proc. ACM SIGMOD Int. Conf. Manag. Data1209–1220(2013) · DOI
  182. Sun G. et al.: Incremental graph pattern based node matching with multiple updates. IEEE Trans. Knowl. Data Eng. 1 (2019) · DOI
  183. 10.1016/j.knosys.2018.10.010 · DOI

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Metadata

Authors
Wang, J., Wang, X., Ma, C., & Kou, L.
Venue
IET Generation, Transmission & Distribution
Published
2020-12-03
Volume
15
Issue
3
Pages
383-407
ISSN
  • 1751-8687
  • 1751-8695
Cited by
111
References
3
Language
en

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Wang, J., Wang, X., Ma, C., & Kou, L. (2021). A survey on the development status and application prospects of knowledge graph in smart grids. IET Generation, Transmission & Distribution. https://doi.org/10.1049/gtd2.12040