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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Enhancing Reliability and Performance of Load Frequency Control in Aging Multi-Area Power Systems under Cyber-Attacks
Applied sciences, Vol.14(19), 8631
25/09/2024
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Abstract
This paper addresses the practical issue of load frequency control (LFC) in multi-area power systems with degraded actuators and sensors under cyber-attacks. A time-varying approximation model is developed to capture the variability in component degradation paths across different operational scenarios, and an optimal controller is constructed to manage stochastic degradation across subareas simultaneously. To assess the reliability of the proposed scheme, both Monte Carlo simulation and particle swarm optimization techniques are utilized. The methodology distinguishes itself by four principal attributes: (i) a time-varying degradation model that broadens the application from single-area to multi-area systems; (ii) the integration of physical constraints within the degradation model, which enhances the realism and practicality compared to existing methods; (iii) the sensor suffers from fault data injection attacks; and (iv) an optimal controller that leverages particle swarm optimization to effectively balance reliability and system performance, thereby improving both stability and reliability. This method has demonstrated its effectiveness and advantages in mitigating load disturbances, achieving its objectives in just one-third of the time required by established benchmarks. The case study validates the applicability of the proposed approach and demonstrates its efficacy in mitigating load disturbance amidst stochastic degradation in actuators and sensors under FDIA cyber-attacks.
Details
- Title
- Enhancing Reliability and Performance of Load Frequency Control in Aging Multi-Area Power Systems under Cyber-Attacks
- Creators
- Di Wu - Peking UniversityFusen Guo - Swinburne University of TechnologyZeming Yao - Swinburne University of TechnologyDi Zhu - Australian National UniversityZhibo Zhang - University of NSWLin Li - RMIT UniversityXiaoyi Du - University of NSWJun Zhang - Swinburne University of Technology
- Publication Details
- Applied sciences, Vol.14(19), 8631
- Publisher
- MDPI AG
- Grant note
- This research is partially supported by the Australian Research Council (ARC) Research Hub for Integrated Energy Storage Solutions (IH180100020).
- Identifiers
- 991013225624902368
- Copyright
- Copyright: © 2024 by the authors
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article