Deep Semi-Supervised Learning Method for False Data Detection Against Forgery and Concealing of Faults in Cyber-Physical Power Systems-Reference-Cited by-同舟云学术

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Deep Semi-Supervised Learning Method for False Data Detection Against Forgery and Concealing of Faults in Cyber-Physical Power Systems

Published:2024-01 Issue:1 Volume:15 Page:944-958
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Wang Guoteng¹^ORCID,Wang Chongyu¹^ORCID,Shahidehpour Mohammad²^ORCID,Lin Wei³^ORCID

Affiliation:

1. College of Electrical Engineering, Zhejiang University, Hangzhou, China

2. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL, USA

3. Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10372481/10153791.pdf?arnumber=10153791

Reference44 articles.

1. Smart Grids: A Cyber–Physical Systems Perspective

2. Cyber–Physical Security of a Smart Grid Infrastructure

3. A Review of False Data Injection Attacks Against Modern Power Systems

4. Cyber–Physical System Security for the Electric Power Grid

5. False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Long-short term memory networks aided fault detection of power facilities;Intelligent Systems with Applications;2024-06

2. Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects;IEEE Transactions on Industry Applications;2024-05

3. Stealthy False Data Injection Attacks Against the Summation Detector in Cyber-Physical Systems;IEEE Transactions on Industrial Cyber-Physical Systems;2024

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