Nowadays, the renewable energy sources in microgrids (MGs) have high participation to supply the consumer’s demand. In such MGs, the problems such as the system frequency stability, inertia, and damping reduction are threatened. To overcome this challenge, employing the virtual inertia control (VIC) concept in the MG structure could be considered as a viable solution to improve the system frequency response. Hence, this work proposes a novel modeling for VIC in an islanded MG that provides simultaneous emulation of the primary frequency control, virtual inertia, and damping. To show the efficiency of the proposed technique, a comparison is made between the dynamic performance of the proposed VIC and conventional VIC under different scenarios. The results indicate that the proposed VIC presents superior frequency performance in comparison with conventional VIC. In addition to VIC modeling, a new cascade controller based on three-degrees of freedom and fractional-order controllers (FOCs) is proposed as an MG secondary controller. The effectiveness of the proposed controller is compared to tilt-integral-derivative and FO proportional-integral-derivative controllers. The Squirrel search algorithm is utilized to obtain the optimal coefficients of the controllers. The results demonstrate that the proposed controller improves the MG frequency performance over other controllers. Eventually, the sensitivity analysis is performed to investigate the robustness of the proposed controller in the face of the variations of the parameters.

Nowadays, marine vessels are critical to the industries and trade of many countries. Modern ships require clean energy and modern communications systems to function properly. The use of renewable energy resources requires efficient coordination using reliable communications, which can be threatened by cyberattacks. An attacker's ultimate goal is to collapse systems by implementing hybrid cyberattacks. Accordingly, this article studies the impact of hybrid cyberattacks involving a combination of denial of service and false data injection, as well as the implementation of attacks on both actuator and measurement signals for load frequency control performance in a low-emission shipboard microgrid. In this regard, a parallel attack detection strategy based on machine learning is developed to detect the attacks imposed on the measurement and actuator signals. A novel resilient control scheme, called adaptive reinforcement learning active disturbance rejection control (ADRC), is also presented to compensate for the impacts of attacks on the system frequency performance and ensure the ship can operate under different attack scenarios. Due to the simplicity and ease of implementation of ADRC, the ability to use reinforcement learning to tune parameters, and the resilience of the technique against attacks, the proposed strategy is a viable method as opposed to other mathematically complicated methods. The feasibility of the proposed method under various conditions has been evaluated by hardware-in-the-loop real-time simulations.

This study proposes a two-level defense strategy to cope with false data injection (FDI) attacks applied to the control and measurement signals of a two-area load frequency control (LFC) system. In the first level, the combination of a recursive estimator, a residual signal-based detector, and a model-free observer is proposed to detect the attacks and mitigate the attacks’ impacts on the system’s measurement signals and secondary control commands. Simultaneously with the first level, a novel model-free independent defense strategy, fractional-order brain emotional learning (FOBEL), is developed in the second level to improve the first-level mitigation performance. The FOBEL controller can make robust and fast decisions in systems facing cyber-attacks. Selecting the appropriate values for the FOBEL controller is vital in improving its performance. To this end, a soft actor-critic deep reinforcement learning (SAC-DRL) algorithm is also employed to tune the FOBEL controller’s coefficients. The proposed two-level defense scheme efficiency is evaluated via real-time simulations in the OPAL-RT simulator and compared with the various detection and mitigation methods under different scenarios. The experimental results reveal that the presented defense strategy successfully detects FDI attacks and performs significantly better in mitigating them than the other strategies.

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

This brief investigates the load frequency control (LFC) issue of an interconnected power system under sensor and actuator false data injection (FDI) attacks. To this end, a novel model-free control method, ultra-local model-free predictive control (ULMFPC), is proposed as the secondary frequency controller to deal with the coordinated FDI attacks and system physical limitations. In contrast to the approaches available in the literature, the presented ULMFPC approach does not need the state estimation of observers and the mathematical model of the system. This method can independently cope with disruptions injected into the system. The ULMFPC considers the employed plant as a dependent system and utilizes predictive control in a constantly updated linear model to track the reference and control limitation. Unlike other predictive control methods, there is no training data set in the presented control strategy, and only small-scale convex optimization is essential for control computation. The proposed control method performance is evaluated by the Speedgoat real-time emulator and compared with the MPC and distributed economic MPC (DEMPC) methods under sensor and actuator FDI attacks. The experimental results indicate that the proposed ULMFPC technique provides much better dynamic responses compared to other methods.