An overview of different strategies applied to enhance the fault Ride-Through (FRT) ability of the Doubly Fed Induction Generators (DFIGs)-based Wind Turbines (WTs) during transient state is introduced in the study. Different FRT strategies dependent on (a) additional protection circuit's establishment, (b) reactive power injecting devices installation, and (c) various control approaches have been proposed in the literature. Typically, during disturbance in the grid to restrict the generated rotor overcurrent and undesirable Direct Current (DC)-link overvoltage, the protection circuits or control structures are connected. Concurrently, to improve the transient performance of the DFIG-based WT, the reactive power injecting devices outperform any deficiency of the reactive power and consequently bound the DC-link voltage and current in the rotor. Actually, during the transient state without damaging its operating strategy, many research findings show a productive DFIG protection. Along these lines, this study centers around underscoring the present status of the rotor overcurrent and DC-link overvoltage protection solutions, for example, the crowbar and its related protection circuits, fault current limiter, the reactive power injecting devices, for example, the static synchronous compensators, static var compensator, and dynamic voltage restorer.

In this manuscript, an Energy Management System (EMS) with Internet of Things (IoT) framework in distribution system (DS) based on hybrid technique is proposed. The proposed method is the combination of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial Tree (FAT) algorithm; therefore called as DDFAT method. The main intention of proposed method is “to optimize the power control and DS resources by constantly tracking the data into communication framework based on IoT”. In this work, the DS is inter connected to the data acquisition method that is the Internet of things uses single IP address resulting in mesh wireless network devices. The internet of things based communication scheme used to facilitate the growth of demand response (DR) for energy management (EM) DS. The transmitting data is carried out by DDFAT method. In this way, the IoT distribution scheme increases the network flexibility and offers optimal utilization of the accessible resources. Moreover, the DDFAT method is reliable to meet global supply along energy demand. The DDFAT method is implemented in MATLAB Simulink platform under three test cases and its performance is analysed with the existing improved artificial bee colony (IABC), squirrel optimization with gravitational search–assisted neural network (SOGSNN), particle swarm optimization (PSO)–assisted artificial neural network (ANN), Fruit fly Optimization algorithm (FOA), and grasshopper optimization algorithm (GOAPSNN) methods.