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.

This paper proposes a distributed reactive power control based approach to deploy Volt/VAr optimization (VVO) / Conservation Voltage Reduction (CVR) algorithm in a distribution network ...

This paper proposes a computationally efficient method for supervisory control of Volt/Var control devices-voltage regulators and capacitor banks - on a distribution system. The method searches for the best control settings for these devices as the operating conditions change on the system in real-time. The objective of the supervisory control is that of a volt/var optimization - minimize the power losses on the distribution circuit while keeping the node voltages within the acceptable range. The method considers multiple voltage regulators and capacitor banks on a distribution feeder. The test results show the effectiveness of method in terms of providing the global optimal solution with fast computational speed.

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.