Renewable energy sources have become a significant alternative energy source due to the continuing depletion of conventional energy sources and fluctuation in fuel costs. Currently, wind energy is the foremost among all other renewable energy sources. However, modeling and analyzing industrial wind turbines is complex as the wind turbine power ratio and size have steadily increased. Undoubtedly, industrial wind turbines are huge and challenging to keep in research labs; simultaneously, exploring the controller/power converter performance is practically impossible. Therefore, to overcome the above drawbacks, wind turbine emulators have been developed to achieve the static and dynamic characteristics of wind energy conversion systems. This paper aims to present a comprehensive review of the different wind turbine emulators available in the literature. In addition, the implementation of real-time emulators is classified according to the structure and approaches. Furthermore, an extensive analysis of the emulators was presented based on the significant parameters utilized for the real-time wind turbine emulators. Finally, this review analyzes the different emulator topologies according to cost, accuracy, complexity, and hardware implementation.

Recently, Electric Vehicles (EV) have pulled in a great deal of consideration attributable to the utilization of clean energy. However, the challenge is that developing number of EVs implies request in charging is tremendous which will aggregate the power grid load. In this paper, a renewable based DC charging system is introduced which integrates power from sun, wind and battery energy storage system (BESS) to provide an uninterruptible power supply for the load. Variable irradiance and wind speeds are considered for this study. Incremental conductance method with boost converter and hill climb method with buck converter are implemented in solar and wind respectively to maximize the system efficiency. Three phase uncontrolled diode bridge rectifier is used to convert the AC power output of wind to DC power. Surplus amount of power generated is preserved in the battery storage system which is retrieved in the absence of renewable sources. A bidirectional converter is designed to allow the forward and reverse flow of power. A controller is designed using PID to limit the discharge current of the storage system. Modelling and validation of proposed system is done using MATLAB Simulink platform.

Blackouts could occur due to various faults and in order to keep the power stations running constantly to avoid consumers losing power a backup plan is necessary. Black start is the process of restoring power to the electric power stations. The Modular Multilevel Converter (MMC) topology has been propitious technology in High Voltage DC (HVDC) system. The research in MMC technology has significantly increased. The MMC based HVDC system has many advantages when compared to the other technology based HVDC system. In this paper, Black Start Operation using a MMC Based HVDC system is proposed to provide continuous power to the load for a certain period of time within which alternative methods of power generation can be made ready. The paper also provides a mathematical model of a continuous equivalent circuit of the MMC Phase Leg. The efficiency of the proposed idea has been verified by MATLAB/Simulink Software and discussed.

Modular multilevel converters are attractive for medium- and high-voltage applications because of their modularity and high-power quality. Reliability is a critical issue for this converter because each submodule has a capacitor, and condition monitoring of these capacitors is highly challenging. There are some conventional techniques for monitoring the condition of submodule capacitors. However, the implementation of these techniques requires additional hardware or inclusion of a reference submodule. Therefore, this study proposes a machine learning-based condition monitoring technique for submodule capacitors in modular multilevel converters. A comprehensive dataset of submodule voltages and arm currents has been collected to extract statistical features and wavelet decomposition techniques are employed to remove the noise in the signals. Furthermore, a comparative analysis has been performed to show the efficacy of the different classifier models. The performance of the classifiers is examined through different indexes, such as accuracy, precision, \(F_1\) score, recall, kappa coefficient, and area under the curve. The results show that the random forest is the most reliable and robust classifier for detecting significant levels of degradation of submodule capacitors in modular multilevel converters.