首页> 外文期刊>Journal of the Franklin Institute >A novel trust-based false data detection method for power systems under false data injection attacks
【24h】

A novel trust-based false data detection method for power systems under false data injection attacks

机译:虚假数据注入攻击下电力系统的基于信任的虚假数据检测方法

获取原文
获取原文并翻译 | 示例
       

摘要

This paper proposes a novel trust-based false data detection method for power systems under false data injection attacks (FDIAs). In order to eliminate the interference posed by false data to the power system in the state estimation process, a trust model is first established to estimate the reliability of the system bus. Then an algorithm is proposed to update the bus trust value, when all the trust value of neighbor buses at one bus node are quite low, then this bus is diagnosed as a malicious node and the false data are detected. This method guarantees that the power systems can estimate the state accurately against FDIAs based on the trust of bus. The simulations on the benchmark IEEE 14-bus, IEEE 30-bus and IEEE 57-bus test systems are used to demonstrate the feasibility and effectiveness of proposed algorithm. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:本文提出了一种基于基于信任的虚假数据检测方法,用于虚假数据喷射攻击(FDIAS)的电力系统。 为了在状态估计过程中消除由虚假数据构成的干扰,首先建立信任模型以估计系统总线的可靠性。 然后提出了一种算法来更新总线信任值,当一个总线节点的邻居总线的所有信任值非常低时,该总线被诊断为恶意节点,检测到错误数据。 该方法保证电源系统可以基于总线的信任来准确地估计状态。 基准IEEE 14公交车的模拟,IEEE 30总线和IEEE 57总线测试系统用于展示所提出的算法的可行性和有效性。 (c)2019年富兰克林学院。 elsevier有限公司出版。保留所有权利。

著录项

  • 来源
    《Journal of the Franklin Institute》 |2021年第1期|56-73|共18页
  • 作者单位

    Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200072 Peoples R China;

    Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200072 Peoples R China;

    Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200072 Peoples R China;

    North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China;

    Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号