Zhang Huifeng, Yue Dong, Dou Chunxia, Hancke Gerhard P
IEEE Trans Neural Netw Learn Syst. 2022 May 27;PP. doi: 10.1109/TNNLS.2022.3175917.
The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology's view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.
需求侧日益增加的虚假数据注入（FDI）攻击给互联微电网的能量管理带来了巨大挑战。为解决这些问题，本文提出了一种基于分布式深度强化学习（DRL）方法的弹性最优防御策略。为评估FDI攻击对需求响应（DR）的影响，提出了一种基于递归最小二乘（RLS）方法的在线评估方法，以评估微电网系统的供电安全或电压稳定性受FDI攻击的影响程度。在评估的安全置信度基础上，提出了一种分布式智能体网络学习方法来推导最优网络权重，从而生成最优防御方案，以确保微电网系统的经济性和安全性。从方法学角度来看，它还可以增强每个微电网的自主性并提高DRL效率。根据这些仿真结果，可以看出所提方法能够很好地评估FDI攻击的影响，改进的分布式DRL方法对于微电网抵御需求侧FDI攻击的最优防御而言是一种可行且有前景的方法。
