Spatio-temporal Data Analysis and Intelligent Optimization Theory for Logistics

Tree structure grid minimal cost repair problem and its corresponding algorithm based on fault prediction

  • Yang CAI ,
  • Danhong TANG ,
  • Jiajun CHEN ,
  • Zhixin XU ,
  • Limeng YANG ,
  • Ming WANG ,
  • Xueming ZHOU ,
  • Dingjiang HUANG
Expand
  • 1. Jinshan Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai 200540, China
    2. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2022-07-20

  Accepted date: 2022-07-20

  Online published: 2022-09-26

Abstract

A long short-term memory (LSTM)-based network employing a fault prediction algorithm and tree-structured network employing a minimal cost repair generation algorithm are proposed in this study to predict possible anomalies using a large amount of historical data for the effective identification of fault treatments. In addition, the minimal cost repair operation sequence was generated based on dynamic programming; the sequence of valid operation orders could be quickly generated. The results of this study indicate that the proposed networks could effectively reduce the dispatch error rate, improve the dispatch efficiency, and reduce the failure time of power grid systems, and therefore can be used to reduce the economic loss caused by the aforementioned factors.

Cite this article

Yang CAI , Danhong TANG , Jiajun CHEN , Zhixin XU , Limeng YANG , Ming WANG , Xueming ZHOU , Dingjiang HUANG . Tree structure grid minimal cost repair problem and its corresponding algorithm based on fault prediction[J]. Journal of East China Normal University(Natural Science), 2022 , 2022(5) : 208 -218 . DOI: 10.3969/j.issn.1000-5641.2022.05.017

References

1 国网河南省电力公司灵宝市供电公司, 三峡大学. 基于大数据驱动的电网故障预测和诊断方法: CN202110786418.X [P]. 2021-11-02.
2 国网甘肃省电力公司武威供电公司. 一种基于大数据技术的配电网故障预测系统及方法: CN202111031875.4 [P]. 2022-01-07.
3 谭红岩. 配电网故障定位的算法研究 [D]. 成都: 西南交通大学, 2012.
4 国网山西省电力公司电力科学研究院, 国网山西省电力公司, 山西合力创新科技股份有限公司. 基于时间序列和故障树分析的电网调控数据中心业务特性故障定位方法及系统: CN202010489886.6 [P]. 2020-09-01.
5 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司. 一种配电网故障预测的方法: CN202110716310.3 [P]. 2021-09-07.
6 张学工. 关于统计学习理论与支持向量机. 自动化学报, 2000, 26 (1): 32- 42.
7 李元诚, 方廷健, 于尔铿. 短期负荷预测的支持向量机方法研究. 中国电机工程学报, 2003, 23 (6): 55- 59.
8 SAFAVIAN S R, LANDGREBE D. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991, 21 (3): 660- 674.
9 苗夺谦, 王珏. 基于粗糙集的多变量决策树构造方法. 软件学报, 1997, (6): 26- 32.
10 广西电网有限责任公司电力科学研究院. 基于随机森林和多层架构聚类的配电网故障预测方法: CN202111122275.9 [P]. 2022-01-04.
11 李兆桐, 张卫山, 郭武武. 基于LSTM的工业互联网设备工作状态预测. 计算机与现代化, 2020, (1): 1- 5.
12 吴海洋, 陈鹏, 郭波, 等. 基于注意力机制和LSTM的电力通信设备状态预测. 计算机与现代化, 2020, (10): 12- 16.
13 国家电网公司, 国网技术学院. 基于与或树模型的电网故障诊断方法: CN201510048392.3 [P]. 2015-05-27.
14 徐海, 王宏, 梁志珊. 基于启发式搜索的配电网供电恢复系统. 东北电力学院学报, 2001, 21 (2): 8- 12.
15 黄宗君. 基于最小生成树理论的配电网故障恢复算法. 继电器, 2003, 31 (12): 9- 12.
16 广东电网有限责任公司东莞供电局. 一种基于决策树算法的电网配变故障诊断方法及系统: CN202011231674.4 [P]. 2021-03-09.
17 江苏省电力公司苏州供电公司, 国家电网公司, 江苏省电力公司, 等. 基于规则树的电网故障设备分析推理方法: CN201510218241.8 [P]. 2015-07-01.
18 QIAN J, ZHU B, LI Y, et al. Fault dynamic monitoring of intelligent power system telecontrol dispatching based on improved fault tree [J]. Journal of Physics: Conference Series, 2021, 1846(1): 012083.
19 ZHOU H, ZHANG S, PENG J, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting [EB/OL]. (2021-03-28)[2022-07-03]. http://www.researchgate.net/publication/347125466.
20 VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 30: 6000-6010.
Outlines

/