Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLAB^TM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

The trend toward Smart grid (SG) is increasing significantly by incorporating Distributed Generators (DGs), which leads to new challenges, especially in protection systems. SGs should strengthen robust environments against cybersecurity threats. So, the cybersecurity of future SGs is essential. This paper proposes a multi-layer protection scheme for the Medium Voltage (MV) Distribution System (DS), especially with reconfigurable SGs. The main protection algorithm is based on Artificial Intelligence (AI), utilizing the communication between all protective devices (PDs) in the grid, whereas as backup protection, another AI algorithm employs the communication between the PDs in the same line. Then, as alternative protection to provide the protection system with another level of security in case of communication issues or cyberattacks, a third algorithm based on the local data of each PDs is proposed. Both simulations using MATLAB SIMULINK and experimental results utilizing a scaled physical grid validated the protection algorithms. The scaled grid has been designed for the smart grid in order to test the behavior of the protection scheme experimentally.

The aim of this paper is to analyze the situations in which distributed power generation systems (DPGSs) based on renewable energy sources (RESs) can be controlled when operating under voltage sags. Analytical models for both solar photovoltaic (PV) system and doubly-fed induction generator (DFIG)-based wind turbine (WT) written in the complex form of the Park variables are given. Three kinds of control for the grid-side converter (GSC) of a PV system are compared: constant forward voltage control (CFVC), balanced positive -sequence control (BPSC) and the proposed BPSC with grid code requirements (BPSC-GCR). Regarding the rotor-side converter (RSC) of a DFIG-based WT, its controllability is studied considering three different-sized DFIG-based WT units: 6 MW (offshore), 2 MW (onshore) and 7.5 kW (setup). The converter limits are also considered. Simulations carried out in MATLAB reveal that a RES-based DPGS can achieve fault ride-through (FRT) when subject to a certain fault (i.e., with a specific duration and depth), but it may be uncontrollable for different-sized units operating under different faults without considering the grid code requirements. Finally, experimental results prove the robustness of the BPSC-GCR method to let GSCs of PV systems achieve FRT under sags.

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions.

Transformer Rectifier Units (TRUs) are a reliable way for DC generation in several electric applications. These units are formed by multiple three-phase uncontrolled bridge rectifiers connected according to two main topologies (parallel and series), and fed by a phase-shifting transformer, which can have different configurations. Fault diagnosis of the uncontrolled bridge rectifier diodes is one of the most important concerns on the electronic devices, nonetheless, rectifier units are inherently not protected in front of Open-Circuit (O/C) faults, which cause malfunction and performance deterioration. In order to solve this drawback, the proposed fault diagnosis method is based on the O/C fault signature observed in the DC-link output voltage of TRUs rectifier. It allows detecting the O/C diodes of parallel and series TRUs with different phase-shifting transformer configurations and for the most usual fault scenarios. Moreover, it also helps the prediction of diodes that could be exposed to failure after the fault, which provides corrective maintenance for the TRU development. The proposed method is illustrated from MATLABTM numerical simulations of a 12-pulse TRU, and is validated with experimental tests.

Recently, the global concern for protection coordination is growing with the impact of distributed generators connected to medium voltage distribution system. Effective protection strategies need to be developed in order to avoid undesirable tripping when distributed generators based on Voltage Source Inverters are connected to the medium voltage grid. According to Spanish grid code requirements, the inverter controller response in this paper is assessed under grid faults integrating low voltage ride through capabilities. This paper presents a novel use of a communication‐based directional relay system with artificial neural network, an appropriate option for smart grids protection. A protection strategy is proposed using two algorithms. The first algorithm is based on gathering data of all the protective devices in the grid and send it to a centralized controller. The second algorithm is based on a zone controller using the communication between the peer protection devices in the same line. One of the main advantages of the zone controller is that no need to modify the protection devices setting in case of temporary grid reconfigurations. The behaviour of the protection algorithms is validated through both simulations in MATLAB‐Simulink and experimental results.