This paper proposes a cyber-physical architecture for the secured social operation of isolated hybrid microgrids (HMGs). On the physical side of the proposed architecture, an optimal scheduling scheme considering various renewable energy sources (RESs) and fossil fuel-based distributed generation units (DGs) is proposed. Regarding the cyber layer of MGs, a wireless architecture based on low range wide area (LORA) technology is introduced for advanced metering infrastructure (AMI) in smart electricity grids. In the proposed architecture, the LORA data frame is described in detail and designed for the application of smart meters considering DGs and ac-dc converters. Additionally, since the cyber layer of smart grids is highly vulnerable to cyber-attacks, t1his paper proposes a deep-learning-based cyber-attack detection model (CADM) based on bidirectional long short-term memory (BLSTM) and sequential hypothesis testing (SHT) to detect false data injection attacks (FDIA) on the smart meters within AMI. The performance of the proposed energy management architecture is evaluated using the IEEE 33-bus test system. In order to investigate the effect of FDIA on the isolated HMGs and highlight the interactions between the cyber layer and physical layer, an FDIA is launched against the test system. The results showed that a successful attack can highly damage the system and cause widespread load shedding. Also, the performance of the proposed CADM is examined using a real-world dataset. Results prove the effectiveness of the proposed CADM in detecting the attacks using only two samples.

翻译：本文提出了一种用于孤立混合微电网安全运行的信息物理架构。在架构的物理层面，提出了一种考虑多种可再生能源和化石燃料分布式发电单元的最优调度方案。在微电网的信息层面，引入了一种基于低功率广域网络技术的无线架构，用于智能电网的高级计量基础设施。在所提出的架构中，详细描述了LORA数据帧，并针对含分布式发电单元和交直流变换器的智能电表应用进行了设计。此外，由于智能电网信息层极易遭受网络攻击，本文提出了一种基于双向长短期记忆网络和序贯假设检验的深度学习网络攻击检测模型，用于检测高级计量基础设施中智能电表上的虚假数据注入攻击。采用IEEE 33节点测试系统对所提能量管理架构的性能进行了评估。为探究虚假数据注入攻击对孤立混合微电网的影响并揭示信息层与物理层之间的交互，对测试系统发动了一次虚假数据注入攻击。结果表明，成功攻击会严重破坏系统并导致大规模甩负荷。同时，使用真实数据集对所提攻击检测模型的性能进行了测试。结果证明，所提攻击检测模型仅需两个样本即可有效检测攻击。