Construction and application of knowledge graph for intelligent dispatching and control

被引:0
|
作者
Yu J. [1 ,2 ]
Wang X. [3 ]
Zhang Y. [1 ,2 ]
Liu Y. [1 ,2 ]
Zhao S. [1 ,2 ]
Shan L. [1 ,2 ]
机构
[1] NARI Group Corporation Co., Ltd., State Grid Electric Power Research Institute Co., Ltd., Nanjing
[2] Beijing Kedong Electric Power Control System Co., Ltd., Beijing
[3] Inner Mongolia Power (Group) Co., Ltd., Hohhot
关键词
Intelligent dispatching and control; Knowledge graph; Knowledge inference; Line fault handling;
D O I
10.19783/j.cnki.pspc.191200
中图分类号
学科分类号
摘要
Knowledge graph technology is a new method describing the complex relationship between concepts and entities in the objective world, which has been widely concerned because of its strong knowledge inference ability. Dispatching and control center is the central hub of power grid operation control. In order to promote the construction of intelligent dispatching and control, combined with the technology of knowledge graph and the specific situation in the field of dispatching and control, this paper proposes the construction method of knowledge graph oriented to the field of intelligent dispatching and control. Furthermore, the application solutions of knowledge graph for supporting the scenario of fault handling, operational rule electronization, breaker operation and dialogue question and answer are put forward. Finally, the knowledge graph of line fault handling is constructed, showing that the established knowledge graph can drive the process of line fault handling automatically, and the accuracy of judgment and identification process is higher, which effectively reduces the risk of manual handling. © 2020, Power System Protection and Control Press. All right reserved.
引用
收藏
页码:29 / 35
页数:6
相关论文
共 29 条
  • [1] Zhang X., Liu D., Li B., Et al., The concept of intelligent panoramic system and its application system in modern power grid, Proceedings of the CSEE, 39, 10, pp. 2885-2894, (2019)
  • [2] Zheng Y., Li G., Li Y., Survey of application of deep learning in image recognition, Computer Engineering and Applications, 55, 12, pp. 20-36, (2019)
  • [3] Hou Y., Zhou H., Wang Z., Overview of speech recognition based on deep learning, Application Research of Computers, 34, 8, pp. 2241-2246, (2017)
  • [4] Zhang Y., Shan L., Yu J., Et al., Power system state estimation based on fuzzy tree method, Power System Protection and Control, 43, 1, pp. 138-145, (2019)
  • [5] Zhang W., Zhang Y., Bai X., Et al., A robust fuzzy tree method with outlier detection for combustion models and optimization, Chemometrics and Intelligent Laboratory Systems, 158, pp. 130-137, (2016)
  • [6] Yang X., Bin G., Yan X., A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter, Protection and Control of Modern Power Systems, 3, 3, pp. 364-372, (2018)
  • [7] Yu Q., Xu C., Li S., Et al., Application of fuzzy clustering algorithm and support vector machine to short-term forecasting of PV power, Proceedings of the CSU-EPSA, 28, 12, pp. 115-118, (2016)
  • [8] Fu M., Ma H., Mao J., Short-term photovoltaic power forecasting based on similar days and least square support vector machine, Power System Protection and Control, 40, 16, pp. 65-69, (2012)
  • [9] Liu Y., Zhou G., Liu X., Et al., A short-term load forecasting method based on intelligent similar day recognition and deviation correction, Power System Protection and Control, 47, 12, pp. 138-145, (2019)
  • [10] Xing S., Gao G., Zhang Z., Short-term load forecasting model based on double-layer random forest algorithm, Guangdong Electric Power, 32, 9, pp. 160-166, (2019)