Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Photovoltaic (PV) system is one of the reliable alternative sources of energy and its contribution in energy sector is growing rapidly. The performance of PV system depends upon the solar insolation, which will be varying throughout the day, season and year. The biggest challenge is to obtain the maximum power from PV array at varying insolation levels. The maximum power point tracking (MPPT) controller, in association with tracking algorithm will act as a principal element in driving the PV system at maximum power point (MPP). In this paper, the simulation model has been developed and the results were compared for perturb and observe, incremental conductance, extremum seeking control and fuzzy logic controller based MPPT algorithms at different irradiation levels on a 10 KW PV array. The results obtained were analysed in terms of convergence rate and their efficiency to track the MPP.Keywords: Photovoltaic system, MPPT algorithms, perturb and observe, incremental conductance, scalar gradient extremum seeking control, fuzzy logic controller.Article History: Received 3rd Oct 2016; Received in revised form 6th January 2017; Accepted 10th February 2017; Available onlineHow to Cite This Article: Naick, B. K., Chatterjee, T. K. & Chatterjee, K. (2017) Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. Int Journal of Renewable Energy Development, 6(1), 65-74.http://dx.doi.org/10.14710/ijred.6.1.65-7

Formulation and evaluation of floating bioadhesive Doxofylline tablets

In the present work, an attempt has been made to develop gastro retentive floating tablets of Doxofylline .HPMC K4M and carbopol were used as controlled release polymers. All the formulations were prepared by direct compression method on 12 station rotary tablet punching machine. The blend of all the formulations showed god flow properties such as angle of repose, bulk density, tapped density. The prepared tablets were shown good post compression parameters and they passed all the quality control evaluation parameters as per I.P limits. FH 5 was the best optimized floating formulation because it released drug completely in 12hrs.It was also observed that the increasing concentration of polymers had a retarding effect on the drug release from the polymer matrices

Bidirectional Power Flow between Solar-Integrated Grid to Vehicle, Vehicle to Grid, and Vehicle to Home

The increasing adoption of renewable energy sources, such as solar power, coupled with the growing popularity of electric vehicles (EVs), has opened up new opportunities for bidirectional power flow between various energy systems. This research paper explores the bidirectional power flow between a solar-integrated grid, electric vehicles, and residential homes. Specifically, it focuses on the benefits, challenges, and potential applications of power exchange between these entities. The paper discusses the technical aspects, economic implications, and environmental considerations of bidirectional power flow, highlighting the potential for enhanced grid stability, energy efficiency, and carbon footprint reduction. Additionally, the study addresses the impact of bidirectional power flow on grid infrastructure, smart grid technologies, and policy frameworks. By shedding light on the interplay between the solar-integrated grid, electric vehicles, and residential homes, this research paper aims to contribute to the advancement of sustainable and intelligent energy systems

Brain tumor image identification and classification on the internet of medical things using deep learning

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use

Network Intrusion Detection using ML Techniques for Sustainable Information System

Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explores various machine learning algorithms employed for intrusion detection and evaluates their performance. Findings indicate that machine learning algorithms exhibit a significant improvement in intrusion detection accuracy compared to traditional methods, achieving an accuracy rate of approximately 90 percent. It is worth noting that the previous work experienced computational challenges due to the time-consuming nature of the utilized algorithm when processing datasets. In this paper, we propose the exertion of more efficient algorithms to compute datasets, resulting in reduced processing time and increased precision compared to other algorithms to provide sustainability. This approach proves particularly when computational resources are limited or when the relationship between features and target variables is relatively straightforward

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