False Data Detection in a Clustered Smart Grid Using Unscented Kalman Filter
Author:
Affiliation:
1. Internet Commerce Security Laboratory, School of Engineering, Federation University, Mount Helen, VIC, Australia
2. SCT, STEM College, RMIT University, Melbourne, VIC, Australia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09839581.pdf?arnumber=9839581
Cited by 9 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. A Review of Power System False Data Attack Detection Technology Based on Big Data;Information;2024-07-28
2. Research on Intelligent Power Grid Attack Detection System Based on Machine Learning;2024 International Conference on Machine Intelligence and Digital Applications;2024-05-30
3. A detection method for false data injection attacks in power systems based on artificial fish swarm K-means clustering algorithm;2023 9th International Conference on Big Data and Information Analytics (BigDIA);2023-12-15
4. Blended Ensemble Learning for Robust MITM Attack Detection and Classification in Smart Grid;2023 33rd Australasian Universities Power Engineering Conference (AUPEC);2023-09-25
5. A New Robust Adaptive Fading Unscented Kalman Filter for Decentralized Dynamic State Estimation in Power Systems;2023 IEEE International Symposium on Circuits and Systems (ISCAS);2023-05-21
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