Temporal and Spatial Similarity Mining of Power Grid Running Section for Future State Prediction

被引：0
|
作者：
Gu X. [1 ]
Liu T. [1 ]
Li S. [1 ]
Wang T. [2 ]
Yang X. [2 ]
机构：
[1] School of Electrical & Electronic Engineering, North China Electric Power University, Baoding
[2] State Grid Hebei Electric Power Company, Shijiazhuang
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 23期
关键词：
graph representation learning algorithm; Operation state prediction; period division; running section spatial feature; similarity matching;
D O I：
10.19595/j.cnki.1000-6753.tces.211486
中图分类号：
学科分类号：
摘要：
Effective prediction of operation state can provide a data basis for power grid risk prediction and optimal regulation, which is of great significance for ensuring the system safe operation. This paper proposes a method for mining the temporal and spatial similarity of power grid running section for future state prediction. Firstly, the graph representation learning algorithm was used to deeply unsupervised learn the power grid topology and its attribute information, and the attribute vector representing the spatial features of running section was extracted. Further, the spatial feature vectors corresponding to the historical running sections was divided into multiple windows according to different time periods through the sliding time window algorithm. Finally, the similarity of corresponding samples between different windows was calculated from the microscopic and macroscopic perspectives to obtain a group of continuous sections that were most similar to the sections in the current period. The subsequent section of the most similar historical section was used as a reference for the future state of the current power grid. The results validated by IEEE 39-bus system and practical power grid simulation examples show that the proposed method can effectively match the most similar historical sections, then realize the auxiliary prediction of the future operation state. © 2022 Chinese Machine Press. All rights reserved.
引用
收藏
页码：6145 / 6156
页数：11
相关论文
共 25 条
  • [1] Li Ting, Xu Weiting, Liu Xianglong, Et al., Review on planning technology of AC/DC hybrid system with high proportion of renewable energy, Power System Protection and Control, 47, 12, pp. 177-187, (2019)
  • [2] Wang Chen, Kou Peng, Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convdutional neural network and simple recurrent unit, Transactions of China Electrotechnical Society, 35, 13, pp. 2723-2735, (2020)
  • [3] Jin Hongyang, Teng Yun, Leng Ouyang, Et al., Multienergy coordinated energy storage model in zero-waste cities based on situation awareness of source and load uncertainty, Transactions of China Electrotechnical Society, 35, 13, pp. 2830-2842, (2020)
  • [4] Wang Tao, Zhang Shang, Gu Xueping, Et al., Trends identification of power system operating states, Transactions of China Electrotechnical Society, 30, 24, pp. 171-180, (2015)
  • [5] Liang Haiping, Tian Chao, Wang Tieqiang, Et al., Running section similarity matching based on improved K-means algorithm, Electric Power Automation Equipment, 39, 7, pp. 119-124, (2019)
  • [6] Wang Tieqiang, Lu Peng, Cao Xin, Et al., Research on similarity matching of power network operation section applying stacked automatic encoder, Electric Power Construction, 42, 1, pp. 117-124, (2021)
  • [7] Guo Ye, Wu Wenchuan, Zhang Boming, Et al., Power flow forecasting method and its application in early warning and security countermeasure system, Automation of Electric Power Systems, 34, 5, pp. 107-111, (2010)
  • [8] Ahmed A, McFadden F J S, Rayudu R., Weather-dependent power flow algorithm for accurate power system analysis under variable weather conditions, IEEE Transactions on Power System, 34, 4, pp. 2719-2729, (2019)
  • [9] Huang Yupeng, Li Lixin, Wang Lei, Et al., Power network operation trend estimation algorithm based on neutral network and its application, Smart Grid, 47, 6, pp. 113-120, (2019)
  • [10] Wang Yi, Hou Junxian, Ma Shiying, Et al., A method of automatic integration and regulation of power flow data for security and stability check of generation scheduling analysis, Power System Technology, 34, 4, pp. 100-104, (2010)
123