In recent years, cyber-physical systems (CPSs) have attracted intense attention due to their potential applications in many areas. However, the strong reliance on communication networks makes CPSs vulnerable to intentional cyberattacks. Therefore, a great number of attack detection methods have been proposed to enforce security of CPSs. In this article, various false data injection attack detection methods presented for CPSs are investigated and reviewed. According to the knowledge of control information, the controllers of CPSs are categorized as centralized and distributed controllers. Existing centralized attack detection approaches are discussed in terms of i) linear time-invariant systems, ii) actuator and sensor attacks, iii) nonlinear systems, and iv) systems with noise. Furthermore, the development of distributed attack detection is reviewed according to different decoupling methods. Some challenges and future research directions in the context of attack detection approaches are provided.

The increasing demands for reducing greenhouse emissions and improving fuel efficiency of marine transportation have presented opportunities for electric ships. Due to the complexity of multiple power resources coordination, varied propulsion loads, changeable economical, and environmental requirements, power/energy management system (PMS/EMS) becomes essential in both designing and operational processes. The existing literature on PMS/EMS can be categorized into rule-based and optimization-based approaches. Compared to the rule-based PMS/EMS, which relies heavily on human expertise, as well as predefined strategies and priorities, the optimization-based approaches can offer more efficient solutions and are more widely used nowadays. This article provides a comprehensive review of the marine optimization-based power/energy management system and discusses the future trends of PMS/EMS in ship power systems.

Microgrids (MGs), referred to as next-generation power systems, are receiving considerable attention from both industry and academia. Integrated with distributed energy resources (DERs), energy storage systems, and a variety of loads, MGs function as a localized power grid that can be operated independently or connected to utility grids. With the rapid development of technology in communication networks, the framework of MGs tends to be more distributed, intelligent, and tightly integrated with networks. Applications of MGs can be found on the Internet of Things (IoT), Industry 4.0, smart cities, and so on.

DC microgrids are complex systems connecting various renewable energy sources to different types of loads based on distributed networks. However, the strong reliance on communication networks makes dc microgrids vulnerable to intentional cyberattacks. In this article, two typical types of observer-based approaches are presented to address the attack detection problem for distributed dc microgrid systems. The disturbance decoupling approach is utilized to eliminate the influence of unknown load conditions and the coupling effect between distributed generation units (DGUs) to the residuals. The proposed method is easy to design and has less computational complexity. The performance of the provided scheme is validated by a dSPACE-based dc microgrid platform.