Search

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

Operating wind power generation system at optimal power point is essential which is achieved by employing a Maximum Power Point Tracking (MPPT) control strategy. This literature focuses on developing a novel particle swarm optimization algorithm enhanced radial basis function neural network supported TSR based MPPT control strategy for Doubly Fed Induction Generator (DFIG) based wind power generation system. The proposed hybrid MPPT control strategy estimates the effective wind speed and estimates the optimal rotor speed of the wind power generation system to track the maximum power. The proposed controller extremely reduces the speed dissimilarity range of wind power generation system, which leads to rationalizing the pulse width inflection of DFIG rotor side converter. This in turn, increases the system's reliability and delivers an effective power tracking with reduced converter losses. Furthermore, by utilizing the proposed MPPT controller, the converter size can be reduced to 40%. Therefore, the overall cost of the system can be gradually decreased. To validate the performance of the proposed MPPT controller, an extensive simulation study has been carried out under medium and high wind speed conditions in MATLAB/Simulink. The obtained results have been justified using experimental analysis.

This literature presents an improved maximum power point tracking (MPPT) controller based on radial basis function neural network (RBFNN) control strategy to extract optimal power for wind power ge...

Purpose Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2; it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases; the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19. Design/methodology/approach Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA’s Earth Observing System data. Findings The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2. Originality/value Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.

High penetration of wind power generation system into grid network has satisfied enormous energy demand and has necessitated the wind power generation system to remain connected with grid network to maintain essential grid stability, even during grid fault conditions. This paper presents a novel hybrid genetic algorithm optimized Elman neural network controller for proton exchange membrane fuel cell supported customized Dynamic Voltage Restorer (DVR) to improve the fault ride-through capability of Doubly Fed Induction Generator (DFIG) based wind power generation systems. The outstanding aspect of the proposed customized DVR is that it has low heat transmission losses, therefore has a better efficiency up to 3% when compared with the conventional energy storage based DVR. The proposed shrewd DVR adequately rides through the fault by improving voltage regulation and prevents wind turbine termination during grid faults. The effectiveness of the customized DVR and proposed control strategy is analyzed for doubly fed induction generator based wind power generation system under symmetrical and unsymmetrical fault conditions. The operation of customized DVR is confirmed using time-domain simulation study carried out in MATLAB/Simulink environment.

In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to develop a fuzzy logic–based pitch angle control and to develop a static transfer switch to make power balance between the wind power generation system and the loads. The power management control system is a progression of logic expressions, designed based on generating power and load power requirement. The outcome of this work targets at an improved power production, active and reactive power compensation and ensures system load constraints. To validate the proposed control strategy, a detailed simulation study is carried out on a 9-MW wind farm simulation simulated in MATLAB/Simulink environment.

This paper aims at modelling an electronically coupled distributed energy resource with an adaptive protection scheme. The electronically coupled distributed energy resource is a microgrid framework formed by coupling the renewable energy source electronically. Further, the proposed adaptive protection scheme provides a suitable protection to the microgrid for various fault conditions irrespective of the operating mode of the microgrid: namely, grid connected mode and islanded mode. The outstanding aspect of the developed adaptive protection scheme is that it monitors the microgrid and instantly updates relay fault current according to the variations that occur in the system. The proposed adaptive protection scheme also employs auto reclosures, through which the proposed adaptive protection scheme recovers faster from the fault and thereby increases the consistency of the microgrid. The effectiveness of the proposed adaptive protection is studied through the time domain simulations carried out in the PSCAD ⧹ EMTDC software environment.

Frequent variation in the wind flow affects the Wind Turbine (WT) to generate fluctuating output power and this can negatively impact the entire power network. This paper aims at modelling an Enhanced-Elman Neural Network (EENN) based pitch angle controller to mitigate the output power fluctuation in a grid connected Wind Energy Conversion System. The outstanding aspect of the proposed controller is that, they can smoothen the output power fluctuation, when the wind speed is above or below rated speed of the WT. The proposed EENN pitch controller is trained online using Gradient Descent (GD) algorithm and the network learning is carried out using Customized-Particle swarm optimization (C-PSO) algorithm. The C-PSO is adopted, in order to increase the learning capability of the training process by adjusting the networks learning rate. Further, the node connecting weights of the EENN is updated by means of GD algorithm using back-propagation methodology. The performance of the proposed controller is analysed using the simulation studies carried out in MATLAB /Simulink environment.

A four-port optical circulator based on two-dimensional square lattice photonic crystals is reported. It is simple besides the brief framework. The crystalline geometrical structure of the circulator makes 90° non-reciprocal transmissions of electromagnetic waves with low insertion loss and high levels of isolation by the diligence of magneto-optic crystals. The structure is novel because it uses a resonant cavity with a simple design. Also, in comparison to prior models, the proposed four-port circulator utilizes a two-dimensional square lattice crystal structure with a cylindrical ferrite section of the π/4 Faraday angle. The finite element method is used for this anisotropic medium to get the tensor elements in this simulation. The importance of the gyromagnetic properties of ferrite crystals for the non-reciprocal transmission is investigated. Furthermore, the corresponding S-parameters for this circulator are analyzed and reported. Due to the compact size and ease of fabrication, this device can be realized for applications such as splitting and isolation in photonic integrated circuits.