To realize the consumption of renewable energy such as wind power and photovoltaics in the power system, renewable energy integration system via modular multilevel converter (MMC)-based high voltage direct current (MMC-HVDC) has been widely applied. However, with the large-scale grid connection of renewable energy units, sub-synchronous oscillation (SSO) is prone to occur. Aiming at the problem, this paper proposes an SSO suppression strategy for renewable energy integration system via MMC-HVDC based on active disturbance rejection control (ADRC) theory. Using the direct drive permanent magnet synchronous generators (PMSG)-based wind farm integration system via MMC-HVDC as an example, firstly the topology and control system principles of the system are described, and a simulation model is built in PSCAD/EMTDC. Moreover, the SSO mechanism of the system is revealed by Nyquist stability criterion, and the major factors affecting the SSO of the system are simulated and analyzed. Subsequently, an additional sub-synchronous damping controller (ASSDC) is proposed based on ADRC theory. Compared to traditional additional damping controllers, the proposed controller considers disturbances of the system during the designing process and has stronger robustness. In addition, when faults happen, the speed of the system with ASSDC reaching a steady-state operating point rises by 33.7% as compared to the system without ASSDC. Finally, the effectiveness of the proposed suppression strategy is verified through simulation analysis.

As the core component of smart grids, advanced metering infrastructure (AMI) provides the communication and control functions to implement critical services, which makes its security crucial to power companies and customers. An intrusion detection system (IDS) can be applied to monitor abnormal information and trigger an alarm to protect AMI security. However, existing intrusion detection models exhibit a low performance and are commonly trained on cloud servers, which pose a major threat to user privacy and increase the detection delay. To solve these problems, we present a transformer-based intrusion detection model (Transformer-IDM) to improve the performance of intrusion detection. In addition, we integrate 5G technology into the AMI system and propose a hierarchical federated learning intrusion detection system (HFed-IDS) to collaboratively train Transformer-IDM to protect user privacy in the core networks. Finally, extensive experimental results using a real-world intrusion detection dataset demonstrate that the proposed approach is superior to other existing approaches in terms of detection accuracy and communication cost for an IDS.

No abstract

Network slicing enables the multiplexing of independent logical networks on the same physical network infrastructure to provide different network services for different applications. The resource allocation problem involved in network slicing is typically a decision-making problem, falling within the scope of reinforcement learning. The advantage of adapting to dynamic wireless environments makes reinforcement learning a good candidate for problem solving. In this paper, to tackle the constrained mixed integer nonlinear programming problem in network slicing, we propose an augmented Lagrangian-based soft actor–critic (AL-SAC) algorithm. In this algorithm, a hierarchical action selection network is designed to handle the hybrid action space. More importantly, inspired by the augmented Lagrangian method, both neural networks for Lagrange multipliers and a penalty item are introduced to deal with the constraints. Experiment results show that the proposed AL-SAC algorithm can strictly satisfy the constraints, and achieve better performance than other benchmark algorithms.

No abstract