Enhancement of Power Equipment Management Using Knowledge Graph

Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multi-source heterogeneous power equipment related data. A graph-search method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.

[1]  C. Bizer D2R MAP - A Database to RDF Mapping Language , 2003, WWW.

[2]  Walid Saad,et al.  Risk assessment of coordinated cyber-physical attacks against power grids: A stochastic game approach , 2016, 2016 IEEE Industry Applications Society Annual Meeting.

[3]  Yongli Zhu,et al.  Load profile disaggregation by Blind source separation: A wavelets-assisted independent component analysis approach , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[4]  Xinchun Lin,et al.  Optimized control for a DVR to compensate long duration voltage sag with low distortion at the load , 2011, 2011 International Conference on Electrical and Control Engineering.

[5]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[6]  J. Leskovec,et al.  Learning Semantic Graph Mapping for Document Summarization , 2004 .

[7]  Yachen Tang Electric Power: Distribution Emergency Operation , 2018 .

[8]  Xi Chen,et al.  Exploration of Graph Computing in Power System State Estimation , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[9]  Zhiyuan Liu,et al.  Representation Learning of Knowledge Graphs with Entity Descriptions , 2016, AAAI.

[10]  Liang Cheng,et al.  A Novel Method of Fault Location for Single-Phase Microgrids , 2016, IEEE Transactions on Smart Grid.

[11]  Simone Paolo Ponzetto,et al.  BabelNet: Building a Very Large Multilingual Semantic Network , 2010, ACL.

[12]  Rik Van de Walle,et al.  Adding Realtime Coverage to the Google Knowledge Graph , 2012, SEMWEB.

[13]  Zhehan Yi,et al.  A Unified Control and Power Management Scheme for PV-Battery-Based Hybrid Microgrids for Both Grid-Connected and Islanded Modes , 2018, IEEE Transactions on Smart Grid.

[14]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[15]  Jiankang Wang,et al.  Evaluating PEV's impact on long-term cost of grid assets , 2017, 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[16]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[17]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[18]  Xuemin Lin,et al.  Keyword search on structured and semi-structured data , 2009, SIGMOD Conference.

[19]  Priyanka Kumari,et al.  Establishing content traceability for software applications: An approach based on structuring and tracking of configuration elements , 2013, 2013 7th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE).

[20]  Yachen Tang,et al.  Switching reconfiguration of fraud detection within an electrical distribution network , 2017, 2017 Resilience Week (RWS).

[21]  Maria-Esther Vidal,et al.  Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems , 2017, ICEIS.

[22]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.