In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology. Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training. Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations having different patterns, which is challenging. Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data. The unsupervised algorithm is excellent for discovering unidentified assault events since it only uses examples of typical events to train the detection models. The datasets used in our study, which looked at some well-known unsupervised detection methods, helped us assess the performances of different methods. The performance comparison with popular unsupervised algorithms is better at finding attack events if compared with supervised and Deep Learning (DL) algorithms.

The DC microgrid systems include power electronic converters with information and communication technology. The performance of the DC microgrids will be determined by how effectively these converters are controlled. These communication protocols that support microgrids based on cooperative control are highly vulnerable to cyberattacks. False data injection attack (FDIA) is a kind of cyber-attack in which attackers attempt to introduce false data into the targeted DC microgrid in order to shut it down and destabilize it. Furthermore, the inherent characteristics of communication networks in microgrids may result in delays or/and packet drops in data transmissions between Distributed Generation Units (DGU). These network issues can degrade the stability and control loop performance, resulting in microgrid system uncertainty. Limited papers are available in the literature focusing on resilience against cyber-attacks and communication delays for DC microgrids in one platform. Thus, this paper discusses the impact of FDIAs on parallel DC/DC converter-structured DC microgrids that use droop-based control techniques to sustain the required DC voltage level using voltage observer and current regulator techniques. An unconventional delay-dependent stability condition is formulated and the system’s control parameters are adopted using the Lyapunov stability theory and Linear matrix inequality (LMI). The proposed method is tested under various circumstances, physical actions, and cyber-attacks, such as load changing, communication delay, packet loss, time-varying attacks, etc. The outcomes demonstrate the efficacy of the suggested approach in detecting and mitigating the considered attacks in DC microgrids using the MATLAB/Simulink tool.

In this paper, a protection method for photovoltaic distributed generation (DG) system to fend off from islanding condition caused by the loss of the utility grid has been proposed along with a control scheme. The proposed islanding protection scheme is developed with a Parametric Time-Frequency Analysis (PTFA) technique for detection and an Optimal Probabilistic Neural Network (OPNN) for classification of events. Along with the islanding protection, a nested loop robust control scheme has been proposed for the seamless operation of the DG system during different uncertainty scenarios. The proposed system with islanding protection and robust control scheme has been implemented in MATLAB/SIMULINK environment. The practical feasibility of the proposed method is verified with experimental setup and dSPACE control kit. For demonstrating the superiority of the proposed islanding protection method and robust control scheme for the DG system, a discussion of the results is presented.

This paper proposes a complete designing and developing the current control technique for IEEE 9 bus generation system. It describes the design of a control strategy known as decoupled controller which contains a pre-compensator and observer are added with Linear Quadratic Regulator (LQR). The proposed controller controls active and reactive power and Pulse Width Modulation (PWM) for generation of appropriate switching signals are employed in the performance analysis of individual DG unit to acquiesce the operation of Multibus Microgrid system. The effectiveness of the suggested controller was justified experimentally by using Xilinx System Generator (XSG) via MATLAB/Simulink along with the consideration of the Multibus Microgrid system under grid connected and islanded mode operations.