In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model that is determined using two methods, i.e., the input-state feedback linearization method and the input-output feedback linearization method for the nonlinear modeling of Covid-19. The goal of control in this model is to reduce the percentage or number of infected people and the number of deaths due to Covid-19 disease. Simulation results show that the feedback linearization methods can have positive results in a significant reduction in unfurl of Covid-19. Delay in quarantine of infected people and constant percentage of people who should be quarantined are investigated as two important parameters. Results show that the percentage of infected people decreases 96.3 % and the percentage of deaths decreases 93.6 % when delay in quarantine equals 7 weeks.
Electricity demand is rising in lockstep with global population growth. The present power system, which is almost a century old, faces numerous issues in maintaining a steady supply of electricity from huge power plants to customers. To meet these issues, the electricity industry has enthusiastically embraced the new smart grid concept proposed by engineers. If we can provide a secure smart grid, this movement will be more useful and sustainable. Machine learning, which is a relatively recent era of information technology, has the potential to make smart grids extremely safe. This paper is a literature survey of the application of machine learning in different areas of smart grids. This paper concludes by mentioning the best machine learning algorithms that can be used in different aspects of the smart grid.
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