Search

DC microgrids (DCMGs) recently have a wide range of applicability in modern power systems due to their compatibility with advanced distributed control strategies. However, the distributed control schemes bring significant challenges to the system operation due to their high dependency on data transmission through communication networks, making them vulnerable to cyber-attacks. Because of their wide range of applications, cyber-attack detection in DCMGs has recently gained considerable attention. In most machine learning (ML)-based cyber-attack detection algorithms, accurate real-time output estimation with a low computation burden is critical. In this paper, an Adaptive Neuro-Fuzzy Inference Systems (ANFIS)-based real-time estimator is proposed for DCMGs. The proposed technique is used in a cyber-attack detection and mitigation framework to estimate output voltage and current for DC/DC converters with acceptable accuracy and computational cost. Cyber-attack detection has been achieved through residual analysis, comparing the estimated and measured data. Finally, the effectiveness of the proposed ANFIS-based real-time estimator for the ML-based cyberattack detection frameworks is validated in a MATLAB/Simulink testbed and compared to other supervised ML algorithms.

This paper aims to design a robust internal model-based voltage control (RIMVC) scheme for DC Microgrids (DCMGs) in the presence of unknown external disturbances. Maintaining voltage reference tracking under measurement noise, delays, model parameter uncertainties and unknown external disturbances while the load changes simultaneously is a severe challenge for the DC–DC converters in DCMGs. By developing a modified internal model-based voltage control for DC–DC converters, this work proposes a plug-and-play (PnP) robust voltage control scheme to address the abovementioned challenge. The proposed control method has a cascade structure. In the first step, a modified IMC control is designed to achieve desired tracking performance for a nominal dynamical system. In the next step, the output feedback H∞ control part is added to improve the performance robustness under external disturbances and parameter uncertainties. The efficiency of the proposed control scheme is evaluated using a real-time MATLAB/Simulink testbed, taking into account unknown internal and external disturbances under various rapid voltage reference changes, model parameter uncertainties, constant power loads, system delays and normal load profile changes in multiple case study scenarios.

In this paper, a distributed secondary controller is proposed for DC microgrids (DCMGs) with reduced dependency on information from neighboring units, while achieving voltage consensus, current sharing, and reference voltage tracking tasks. Adopting a distributed finite-time secondary controller from the literature, we use the physical relations in the DCMG network to eliminate the dependency of the local controllers on the voltage information of the neighboring units. This is done by using the local measurements of the load and the unit currents and the line resistances to achieve voltage consensus in the network. Another revision is to modify the control law for the unit responsible for tracking the reference voltage from an external tertiary controller in the interconnected network setting. We propose to release this unit from any responsibilities except for the reference voltage tracking. Finally, we propose to include a saturation function on the secondary controller with an integrator anti-windup logic to maintain the system voltages within a safe limit. Simulation results indicate that the proposed controller provides superior performance compared to the conventional controller in achieving all of the desired control objectives for the DCMGs.