Intelligent Assessment System for Dynamic Security Risk of Large-scale Power Grid

被引：0
|
作者：
Li C. [1 ]
Li H. [1 ]
Liu Y. [1 ]
Wu H. [2 ]
Zhang Q. [2 ]
Fan H. [2 ]
机构：
[1] Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan
[2] State Grid Jiangsu Electric Power Co., Ltd., Nanjing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 22期
基金：
国家重点研发计划;
关键词：
Dynamic security; Intelligent assessment; Large-scale power system; Machine learning;
D O I：
10.7500/AEPS20190507003
中图分类号：
学科分类号：
摘要：
The architecture and key technologies of the intelligent assessment system for dynamic security risk of power systems are studied for preventing and controlling fast dynamic security risk of large-scale AC-DC hybrid systems. The overall framework of intelligent assessment based on machine learning and the structure of unified dynamic security risk assessment model are proposed considering the general assessment process. A training sample set containing fault locations, generation and load modes, and network topology structures is constructed. The sample balance technique is used to improve the accuracy of the assessment model. Advanced features for assessing dynamic security risk are extracted based on deep learning, and a unified assessment model of dynamic security risk is constructed and updated with mainstream machine learning framework. A provincial power grid is taken as an example to verify the feasibility of the proposed intelligent assessment system for dynamic security risk. © 2019 Automation of Electric Power Systems Press.
引用
收藏
页码：67 / 75
页数：8
相关论文
共 48 条
  • [1] Li M., Characteristic analysis and operational control of large-scale hybrid UHV AC/DC power grids, Power System Technology, 40, 4, pp. 985-991, (2016)
  • [2] Li W., Jin C., Wen K., Et al., Active frequency response control under high-power loss, Automation of Electric Power Systems, 42, 8, pp. 22-30, (2018)
  • [3] Xin Y., Shi J., Zhou J., Et al., Technology development trends of smart grid dispatching and control systems, Automation of Electric Power Systems, 39, 1, pp. 2-8, (2015)
  • [4] Zhang B., Wu S., Cai B., Et al., Design of an early warning and security countermeasure system for electric power control centers, Automation of Electric Power Systems, 30, 6, pp. 1-5, (2006)
  • [5] Sun H., Hu J., Liu Y., Et al., Evolution of the power dispatching control center, Automation of Electric Power Systems, 28, 15, pp. 1-6, (2004)
  • [6] Sun H., Huang T., Guo Q., Et al., Power grid intelligent security early warning technology based on big simulation data, Southern Power System Technology, 10, 3, pp. 42-46, (2016)
  • [7] Li B., Xu T., Xue Y., Et al., Engineering design method of integrated and coordinated defense system of large power system security and stability, Automation of Electric Power Systems, 33, 23, pp. 90-93, (2009)
  • [8] Yan J., Yu Z., Tian F., Et al., Dynamic security assessment and early warning system of power system, Proceedings of the CSEE, 28, 34, pp. 87-93, (2008)
  • [9] Cai B., Wu S., Wang S., Et al., Power grid online security and stability analysis and forewarning system, Power System Technology, 31, 2, pp. 36-41, (2007)
  • [10] Tian F., Zhou X., Yu Z., Optimization method of transient stability preventive control based on sensitivity analysis and time domain simulation, Electric Power Automation Equipment, 38, 7, pp. 155-161, (2018)
12345