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The safe operation of AC-DC systems requires the Monitoring of appropriate system signals, the accuracy and rapid classification of any perturbations so that protective control decisions can be made. In case of fast acting HVDC transmission system, such decisions must often be made within tens of milliseconds to guarantee safe operation from disturbances such as the common commutation failures. The detection and fast clearance of faults are important for safe and optimal operation of power systems. Due to the integration of fast acting HVDC systems in ac power systems, it is necessary to detect, classify and clear the faults as fast as possible. The source and cause of disturbances or faults must be known before appropriate mitigation action be taken. For secure operation of a system, a feasible approach is to monitor the signals so that accurate and rapid classification of fault is possible for making correct protective control decisions. However, fast and reliable fault identification is still a big challenge. It is not easy to identify HVDC faults by using pure frequency or pure time domain based methods. The pure frequency domain based methods are not suitable for the time-varying transients and the pure time domain based methods are very easily influenced by noise.Recently, due to advancement of power electronics technology, High Voltage Direct Current (HVDC) transmission technology has been utilized to identify the faults in power system. The HVDC Transmission system is very reliable, flexible and cost effective. Advances in artificial intelligence techniques such as Fuzzy, Neural and ANN etc. and Power Semiconductor devices have made tremendous impact in the identifying of faults in HVDC system. A case is made to present overview of the artificial intelligence techniques to identify the faults in HVDC transmission system.

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The high penetration of renewable energy resources (RESs) based microgrids (MGs) into the modern power system brings severe system frequency fluctuations due to RESs uncertain nature. In such cases, supplying an MG model with an effective load frequency control (LFC) plays a crucial part in regaining the stability of the electrical network. In this work, a wind turbine generator (WTG) and diesel generator (DEG) are efficiently planned as autonomous diesel wind energy-based microgrid (DWMG). A wind-contributed dynamic model, speed regulator, and proportional-integral-derivative (PID) frequency controller are designed to make the WTG system aware of power fluctuations. Additionally, an integral type sliding mode control (I-SMC) is designed to generate the supplementary control action for the frequency regulation against the load and source uncertainties. A recently invented artificial gorilla troops optimizer (GTO) is utilized to obtain the controller parameters. The results reveal the proposed method's benefits, such as least frequency deviations, shorter settling time, and minimum integral errors over state-of-the-art methodologies.

This research study’s objective is to provide a comprehensive analysis of the efforts that have been made to improve the power quality and thermal management of batteries that are operating at low temperatures. These improvements have been made by combining the utilization of conventional solar air energizer (SAE) ducts with the application of a variety of different configurations of longitudinal fins. These expanded surfaces can be found on the absorber or bottom plate surface, and they are placed in a variety of positions along the airflow channel. It does this by increasing the surface area of the typical SAH and making the flow more turbulent, both of which contribute to the improvement in performance. Several studies have been carried out to enhance the thermal efficiency of clear SAE ducts by making use of experimental fins. An effort has been made to establish the Nusselt number and the friction factor by making use of the correlations that the researchers have provided. This was performed so that the performance of various configurations of finned SAEs could be compared to one another.