Volume I Issue VI

A simulation model of static compensator (24-p STATCOM) has been constructed on matlab/simulink software to examine its capability for voltage sag mitigation This paper describes the performance of a Flexible Alternating Current Transmission Systems (FACTS) device, namely, STATic synchronous COMpensator (STATCOM) based on 24-pulse Voltage Source Converter (VSC), for the mitigation of voltage-dip caused by the starting of a 100 HP high power induction motor. It improves the voltage profile feeding to a high power induction motor at starting by injecting a controllable current to the supply line. Its capability to compensate reactive power to the system when the voltage dip occurs due to starting of 100 HP power induction motor load is described. 24-pulse VSC- based STATCOM and implemented it into a power-system consist a 100 HP power induction motor in MATLAB Simulink environment. The results show that the fast response and the STATCOM capability to for mitigate voltage sag.

The automotive industry today requires the change in design, lower cost and advanced features to enhance the demand of an automobile. With the invention of new designs and using the analysis methods the designers today can predict the performance of an automobile. For a tail lamp the thermal analysis has been performed on the heat sink of a tail lamp. A heat sink used in tail lamp functions to dissipate the heat generated due to continuous use of lamp. The analysis has been performed in two steps. A CATIA model of a tail lamp has been generated in the first step which is in a while imported in the HYPERMESH. Later in the analysis meshing is done on the model with the help of HYPERMESHING and after applying boundary conditions the solution report file and graph can be plotted. These report file and graphs helps in detecting the new boundary conditions validation by comparing the graph result with the standard result of 80W street lamp and 56W tail lamp. Hence the validation of the result can be shown via a comparison table.

Have long depended on coal-fired and natural gas power plants to convert chemical fuel into electricity. Now, scientists have found a way to convert electricity into a fuel using excess power from renewables like wind and solar. While methane holds potential as a fuel source, it is also a potent greenhouse gas. It is released en masse from landfills, factory farms and natural gas spills and has more than 20 times the heat-trapping potential of carbon dioxide. But this microbial methane is different, Spormann says. "The carbon for the methane comes from atmospheric CO2. So the methane that is produced by the microbial electrosynthesis is essentially carbon neutral and so will all other commodity chemicals that can be produced that way," he says. The electricity comes from clean energy like wind and solar and the process utilizes electricity that would otherwise be lost. With outdated transmission systems, wind farms and solar photovoltaic power plants often produce more electricity than can be used or stored. In the Pacific Northwest, wind farms were taken offlinethis past spring because of an increase in hydroelectric power from dams due to springtime snow melt and an outdated grid that couldn’t distribute the additional power.

The recent extraordinary growth of artificial intelligence and its applications has been paralleled by a surge of interest in machine learning, a field concerned with the developing computational theories of learning processes and building learning machines. Because the ability to learn is clearly fundamental to any intelligent behavior. The concern and goals of machine learning are central to the progress of artificial intelligence. The basic idea behind the paper is to explore the basics of Machine Learning in Artificial Intelligence. Artificial Intelligence (AI) is “The study and design of Intelligent System”. In simple words Artificial Intelligence can be defined as the process of “Generating Intelligence in Machines”. In order to have intelligence in machines, the machine must have planning, Diagnosis, Speech Recognition, Vision Recognition, Game Playing, NLP (Natural Language Processing) and Prediction for each to work and give efficient result the backbone is Machine Learning. Learning may play a role to develop new systems or make changes in already established systems. Machine Learning has a very important role in Artificial Intelligence, the paper tries to explore the importance of Machine Learning thus to know the role it plays in it. The role of Machine Learning touch both the Psychological aspects and as well as the Technical aspects. The paper touches the topics like types of machine learning methods. At the end an attempt has been made to have a look at the aspects of machine learning. Machine learning has a great future. Recent advances in the field of machine learning method along with successful applications across a wide variety of field such as Bioinformatics, promise powerful new tools for practicing scientist. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near term progress in promising directions..