In the era of growing power demand and power networks, continuous supply of power and data exchanges is inevitable nurturing the implementation of smart grid technology. The smart grid is a self-healing technology which promises the electricity consumers to be an active participant. However, Intelligent Electronic Device (IED) supports the SG as the backbone for operation imparting the functionalities such as protection and monitoring the power networks. But the limitations of the common IED are its finite allocation of functions without modifying its hardware. Moreover, these IEDs confront the communication problems during natural calamities and other network failures as it adopts the cellular communication topology. The limitations mentioned above can be overcome by adopting the flexible IEDs integrated with signal conditioning, filtering and discretization functions along with the implementation of Power Line Carrier communication. The flexible IED and the PLCC extends the scalability of the most reliable and highly efficient smart grid architecture.
Coronavirus is a quickly spreading viral sickness that contaminates people and the day to day existence of individuals, their wellbeing, and the economy of a nation are impacted because of this lethal viral illness. Many endeavors have been directed to track down a reasonable and quick method for recognizing contaminated patients in a beginning phase. The spread of COVID-19 in the entire world has seriously jeopardized the humankind. The assets of the absolute biggest economies are worried because of the huge infectivity and contagiousness of this sickness. The ability of Machine Learning models and Deep Learning models can be actually used to estimate the quantity of impending cases impacted by COVID-19 which is by and by viewed as a likely danger to humankind. Specifically, seven standard determining models, in particular LR, LASSO, SVM, NN, XGB Regressor, Random Forest Regressor have been utilized in this review to estimate the compromising elements of COVID-19. Three kinds of assumptions are made by all of the models, similar to the amount of as of late spoiled cases, the amount of passings, and the amount of recoveries.
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