Intelligent Security Isolation and Risk Management of Secondary Systems in Intelligent Distributed Energy Networks
DOI:
https://doi.org/10.13052/dgaej2156-3306.4046Keywords:
Intelligent distributed energy network, security isolation, Risk management, Deep reinforcement learningAbstract
In the context of the rapid development of intelligent distributed energy resources (IDEN), the safety and reliability of secondary systems have become a key challenge to ensure a stable energy supply. In view of the potential risks of secondary systems in IDEN, this paper proposes an intelligent security protection framework that integrates Logic Rehearsal and Risk Modeling. Firstly, a dynamic logic isolation mechanism was designed based on the deep reinforcement learning algorithm, and the abnormal traffic blocking rate was achieved on the test dataset by simulating the interaction behavior of the internal components of the secondary system in real time, and the probability of system misoperation was reduced to 3.2%, which significantly improved the system boundary protection capability. Secondly, a failure risk assessment model based on Bayesian network was constructed, which integrated historical fault data (covering 5 typical fault scenarios and including 12,000 sample records) and real-time operation parameters, with a prediction accuracy of 89.5% and successfully shortened the risk early warning response time to 0.8 seconds. Experimental results show that the proposed method can improve the overall security of the secondary system by 41.3% and reduce the operation and maintenance cost by 28.6% in the IEEE 33-node distribution network simulation platform. This study provides a theoretical basis and technical support for the security isolation and risk management of intelligent distributed energy networks.
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