Accurate Joint Detection of False Data Injection Attacks on Islanded PV Output Power and State of Health Estimation of Lithium-Ion Batteries-Reference-Cited by-同舟云学术

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Accurate Joint Detection of False Data Injection Attacks on Islanded PV Output Power and State of Health Estimation of Lithium-Ion Batteries

Published:2024-01-08 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)
language:
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Author:

Massaoudi Mohamed¹,Davis Katherine R.¹,Abu-Rub Haitham²,Ghrayeb Ali²,Huang Tingwen³

Affiliation:

1. Texas A&M University,Department of Electrical and Computer Engineering,College Station,USA

2. Texas A&M University at Qatar,Department of Electrical and Computer Engineering,Doha,Qatar

3. Texas A&M University at Qatar,Science Department,Doha,Qatar

Funder

Qatar Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10428118/10428123/10428878.pdf?arnumber=10428878

Reference13 articles.

1. Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

2. An Overview of Cyber-Physical Security of Battery Management Systems and Adoption of Blockchain Technology

3. An Ecological Robustness Oriented Optimal Power Flow for Power Systems’ Survivability

4. Cloud-based battery condition monitoring platform for large-scale lithium-ion battery energy storage systems using internet-of-things (IoT)

5. A Security Perspective on Battery Systems of the Internet of Things

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