Journal article
Distributed Privacy-Preserving Estimation for Multi-Area Power Systems With Multichannel Event-Triggered Mechanisms
IEEE transactions on automation science and engineering, Vol.22, pp.4831-4839
2025
DOI: 10.1109/TASE.2024.3411803
Abstract
In power systems with multi-area power exchanges, distributed state estimation not only depends on sensor measurements but also requires area-interaction. Plentiful transmitted information puts pressure on data privacy and communication resources. To preserve the privacy of sensor and interaction data, a switching encryption, whose minimum dwell-time is less than the decryption time of eavesdroppers, is proposed. Based on encrypted data, multichannel event-triggered mechanisms are established to save the communication resources of sensor-to-estimator and area-interaction channels, simultaneously. By designing the appropriate estimator gains and encryption-compensating mechanisms, the asymptotic stability of estimation error dynamics is guaranteed. Compared with the existing privacy-preserving technologies, the switching encryption improves the estimation accuracy of power system states while avoiding the decryption of eavesdroppers. The effectiveness of distributed privacy-preserving estimation is illustrated by a three-area power system.
Details
- Title: Subtitle
- Distributed Privacy-Preserving Estimation for Multi-Area Power Systems With Multichannel Event-Triggered Mechanisms
- Creators
- Tian-Yu Zhang - Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R ChinaGuang-Hong Yang - Northeastern UniversityDan Ye - Northeastern University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automation science and engineering, Vol.22, pp.4831-4839
- Publisher
- IEEE
- DOI
- 10.1109/TASE.2024.3411803
- ISSN
- 1545-5955
- eISSN
- 1558-3783
- Number of pages
- 9
- Grant note
- 20230301 / Postdoctoral Science Foundation of Northeastern University 2023020359-JH6/1005 / Liaoning Distinguished Young Funds 2018ZCX03 / Research Fund of State Key Laboratory of Synthetical Automation for Process Industries BX20220061; 2023M730520 / China Postdoctoral Science Foundation U23A20337; U1908213 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Electronic publication date
- 06/26/2024
- Date published
- 2025
- Academic Unit
- Computer Science
- Record Identifier
- 9984696725702771
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