A direct current (DC) microgrid has become a superior power system in recent years due to the development of DC loads and higher efficiency of DC systems. One of the challenging problems on DC microgrids operation is protection, and it is still a particular concern associated with the challenges of developing a proper protection scheme owing to its characteristics and lack of standards in DC protection. Due to the significant increasing interest on DC microgrid; this study investigates protection problems and schemes that need to be addressed in modern power systems involving DC microgrids. This study analyses and presents a comprehensive review of the most recent growth in the DC microgrids protection. Additionally, the fault characteristics of DC microgrids, the impact of constant power loads, the protection devices and several proposed methods to overcome the protection problems are discussed. The differences between the proposed protection methods for the DC microgrids are also discussed.

This article proposes a localized protection scheme for dc microgrids with radial configuration under the impact of constant power loads (CPLs) to determine the location of faults accurately. The proposed fault location scheme is primarily designed for fault location of CPLs in dc microgrids. First, a local protection relay for CPL is designed based on the transient behavior of the current and voltage in the main distribution line. Then, the estimation of the fault resistance is formulated based on the power sharing in the system to improve the accuracy of the protection system. To realize a robust protection scheme considering the variation of fault resistance, a fault resistance estimation procedure is employed to design a system that locates both low- and high-impedance faults. Finally, the effectiveness of the proposed strategy is evaluated based on offline digital time-domain simulations in Digsilent PowerFactory software environment and experimentally verified by implementing on a laboratory scale hardware setup. The obtained simulation and experimental test results, and comparison with other methods prove that the proposed scheme is immune against these disturbances and can efficiently and reliably estimate the location and resistance of faults with high accuracy and acceptable error margin.

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.

With increasing environmental problems, including air pollution, rising greenhouse gases and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section contributes to a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are considered as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine together provides optimal power distribution among them to maximise power usage and minimise fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type, including its benefits and drawbacks, is presented by the authors. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.

Accurate locating of the faulty section is desired in dc microgrid due to the presence of power electronic converters and low-impedance cables. Some of the existing schemes consider power sources at only one end of the line; thus, assume that the fault current is injected from only one end of the line. This assumption is not true in the case of meshed dc microgrids, where fault current would be supplied from both ends. Moreover, existing communication-based methods require either a fast communication network or data synchronization. To address the aforementioned issues, this article proposes a novel local fault location scheme for meshed dc microgrids. Low- and high-impedance faults are located by measuring the current by localized intelligent electronic device. Based on the parameter estimation approach, the fault location is estimated by sampling the peak values of the fault current. The effectiveness of the proposed strategy is evaluated based on offline digital time-domain simulations in MATLAB/Simulink software environment for a meshed test microgrid system and experimentally verified by implementing in a laboratory-scale hardware setup. Comparing the proposed method with other existing methods proves the effectiveness of the proposed technique for different types of faults.

Extreme weather events lead to electrical network failures, damages, and long-lasting blackouts. Therefore, enhancement of the resiliency of electrical systems during emergency situations is essential. By using the concept of standby redundancy, this paper proposes two different energy systems for increasing load resiliency during a random blackout. The main contribution of this paper is the techno-economic and environmental comparison of two different resilient energy systems. The first energy system utilizes a typical traditional generator (TG) as a standby component for providing electricity during the blackouts and the second energy system is a grid-connected microgrid consisting of photovoltaic (PV) and battery energy storage (BES) as a standby component. Sensitivity analyses are conducted to investigate the survivability of both energy systems during the blackouts. The objective function minimizes total net present cost (NPC) and cost of energy (COE) by considering the defined constraints of the system for increasing the resiliency. Simulations are performed by HOMER, and results show that for having almost the same resilience enhancement in both systems, the second system, which is a grid-connected microgrid, indicates lower NPC and COE compared to the first system. More comparison details are shown in this paper to highlight the effectiveness and weakness of each resilient energy system.