The present work addresses the modelling, control, and simulation of a microgrid integrated wind power system with Doubly Fed Induction Generator (DFIG) using a hybrid energy storage system. In order to improve the quality of the waveforms (voltages and currents) supplied to the grid, instead of a two level-inverter, the rotor of the DFIG is supplied using a three-level inverter. A new adaptive algorithm based on combined Direct Reactive Power Control (DRPC) and fuzzy logic controls techniques is applied to the proposed topology. In this work, two topologies are proposed. In the first one, the active power injected into the grid is smoothened by using an economical hybrid battery and supercapacitor energy storage system. However, in the second one, the excess wind energy is used to produce and store the hydrogen, and then a solid oxide fuel cell system (SOFC) is utilized to regenerate electricity by using the stored hydrogen when there is not enough wind energy. To avoid overcharging, deep discharging of batteries, to mitigate fluctuations due to wind speed variations, and to fulfil the requirement of the load profile, a power management algorithm is implemented. This algorithm ensures smooth output power in the first topology and service continuity in the second. The modelling and simulation results are presented and analysed using Matlab/Simulink.

A substantial technological growth of the photovoltaic systems has occurred around the world during the recent years enhancing the availability of electric energy in an environment friendly way. Internet of Things (IoT) is one of the emerging technologies having a great potential to apply in the area of renewable energy, especially on solar PV cell. Maximum power production is the general criteria for any solar PV cell. The maximum power point tracking technique enables maximization of the energy production of PV cell during partial shading conditions. Thus, the overall efficiency of the photovoltaic energy production system is increased. Numerous techniques have been proposed during the last decade for implementing the maximum power point tracking process in a photovoltaic system. This article proposed a novel idea of interfacing IOT system with PV cell in partial shading condition and applied MPPT method to analyze and demonstrate their performance features.

Autonomous Vehicles are expected to change the future of worldwide transportation system. As self-driving cars are facing a lot of engineering challenges, it is one of the hottest topics in recent research. One such challenge is to build models to predict the movements of traffic agents such as cars, cyclists, pedestrians etc around the self-driving cars. The objective of this paper is to analyse the prediction efficiency of various deep learning models by calculating root mean square error score. This deep learning models takes a current state of the surrounding and depending on that predict the motion for the agents.

Pulmonary fibrosis is a progressive lungs disease which usually gets worse over time. Once this disease damages the lungs, it cannot be cured totally. But early detection and proper diagnosis can help to keep this disease in control. It causes scarring in the lungs over time. As an effect, people face breathing difficulty. It can cause shortness of breath, even at rest. The general causes of pulmonary fibrosis can be exposure to toxic element like coal dust, asbestos fibres, silica dust, hard metal dusts etc. But in majority of the cases, the doctor cannot figure out the exact cause of this disease. That's why this disease is termed as Idiopathic Pulmonary Fibrosis. The objective of this paper is to analyse and compare the performance of various machine leaning models by predicting the final forced volume capacity measurements for each patient and a confidence value. It can be deployed on any computer to predict a patient's severe condition regarding lungs function which is based on a CT scan of the lungs of the patients. Lung function is checked out based on a spirometer output that measures the forced vital capacity (FVC) of the lungs. In the future, early diagnosis of pulmonary fibrosis should be possible. Machine learning model is helping to use the human resources efficiently and it is also reducing the expanses spent on the social and healthcare aspects of this deadly disease.