Deep Learning Based Fast Security Correction of Power System with Unified Power Flow Controller

Guoqiang SUN; Ke ZHANG; Zhinong WEI; Qun LI; Jiankun LIU; Jingbo ZHAO; Ningyu ZHANG

doi:10.7500/AEPS20200313001

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Deep Learning Based Fast Security Correction of Power System with Unified Power Flow Controller

Application Research | 更新时间：2025-06-16

- Deep Learning Based Fast Security Correction of Power System with Unified Power Flow Controller
- Automation of Electric Power Systems Vol. 44, Issue 19, Pages: 119-127(2020)
- 作者机构：
  
  1.河海大学能源与电气学院，江苏省南京市211100
  2.国网江苏省电力有限公司电力科学研究院，江苏省南京市211103
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20200313001
  CLC：
- Received：13 March 2020，
  
  Revised：2020-06-20，
  
  Published Online：11 August 2020，
  
  Published：10 October 2020
- 稿件说明：
移动端阅览
孙国强,张恪,卫志农等.基于深度学习的含统一潮流控制器的电力系统快速安全校正[J].电力系统自动化,2020,44(19):119-127.

SUN Guoqiang,ZHANG Ke,WEI Zhinong,et al.Deep Learning Based Fast Security Correction of Power System with Unified Power Flow Controller[J].Automation of Electric Power Systems,2020,44(19):119-127.
孙国强,张恪,卫志农等.基于深度学习的含统一潮流控制器的电力系统快速安全校正[J].电力系统自动化,2020,44(19):119-127. DOI： 10.7500/AEPS20200313001.

SUN Guoqiang,ZHANG Ke,WEI Zhinong,et al.Deep Learning Based Fast Security Correction of Power System with Unified Power Flow Controller[J].Automation of Electric Power Systems,2020,44(19):119-127. DOI： 10.7500/AEPS20200313001.

摘要

随着新一代柔性交流输电装置如统一潮流控制器（UPFC）在现代电力系统中的逐步推广和应用，对电网安全校正策略的制定提出了更高的要求。基于物理模型的传统电网安全校正方法在实时性方面具有一定的局限性，而数据驱动方法将大量的复杂计算前移到离线阶段，具有快速在线计算性能。因此，针对含UPFC的电力系统提出了一种基于深度学习的快速安全校正方法。首先，基于深度学习建立了节点调整状态识别模型，利用深度神经网络（DNN）的分类和学习能力，优先确定存在调整可能性的节点范围，避免物理模型优化类方法迭代无解问题。然后，针对缩减后的寻优空间进一步采用优化法实现系统安全校正计算，快速确定系统各节点调整量。基于中国南京西环网UPFC示范工程的应用效果表明，所提快速安全校正策略能够发挥DNN的学习能力，进而提高系统安全校正的效率和实用性。

Abstract

With the gradual popularization and application of new generation of flexible AC transmission devices such as unified power flow controller (UPFC) in modern power system

higher requirements are requested for the formulation of power grid security correction strategy. The traditional power grid security correction method based on physical model has some limitations in the real-time field. The data-driven method moves a large number of complex calculations forward to the offline stage

so it has fast online computing performance. Therefore

a fast security correction method based on deep learning is proposed for power system with UPFC. Firstly

based on deep learning

a recognition model of node adjustment state is established. The ability of classification and learning of deep neural network(DNN) is used. It gives priority to determining the nodes with adjustment possibility

which avoids the iterative non-solution problem of physical model optimization methods. Then

aiming at the reduced optimization space

the optimization method is further used to realize the system security correction calculation

and the adjustment amount of each node in the system is quickly determined. Based on the application results of the UPFC demonstration project of west ring network in Nanjing

China

the proposed fast security correction strategy can facilitate the learning ability of DNN and improve the efficiency and practicability of the system security correction.

关键词

Keywords

references

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