Design Simulation of Improvement of Voltage Profile and Loss Minimization by Efficient Placement of Distributed Generation in Grid Connected System

Electricity consumption is rapidly increasing, and the gap between generation and load is widening. The mismatch between demand and load causes a range of problems, including failure, low power, and, in certain cases, blackout. These issues will be solved by including Distributed Generation (DG) into the system. For maximum dependability, technological and economic benefits, and optimal size and capabilities of distributed generators, the  proper  distribution of  power systems,  kind of generating equipment, number of units, and so on are critical. Among these concerns, the difficulty of placing DG units in the best location and size is critical. Inadequate DG resource distribution to the power system will result in increased power losses. This problem is solved using genetic algorithms. For the conventional 15 bus radial distribution system, the load flow is generated using the backward forward sweep method. Load flow is used to assess the impact of DG size and location on system losses. Machine losses rise as a result of inappropriate DG allocation. As a result, the genetic algorithm (GA), an evolutionary process, is being researched, and an algorithm is being created to discover the appropriate size and position of the distributed generation unit in a radial distribution system. The overall active power losses are reduced, and the voltage profile is improved due to proper DG allocation. Introduces a multi-objective feature that accounts for active power losses, voltage changes, and DG costs, with each variable given a weight. Voltage limits, active power loss constraints, and DG size limitations all affect objective feature minimization. This method is utilized on the conventional 15 bus radial distribution system

Performance Based Grouping of Neighbors Students in Progressive Education Datasets

Now a day’s the educational organizations are facing the biggest challenges, of the massive growth of educational data. Further they do not have a good policy and to use this data for improving the quality of their managerial decisions in today’s scenario. The main goal of higher education institutions is to provide quality of education for their students. In general the educational database contains the important information for predicting a student’s performance, and this Prediction of student’s performance in educational environments is of utmost importance. The knowledge mining techniques has provided a decision making tool which can facilitate better resource utilization in terms of students performance. The knowledge mining techniques are more helpful in classifying educational database. In this paper the clustering task is used to assess student’s performance from education databases. By using this task we extract the knowledge that can describes students’ performance in end semester examination. Keywords – Educational datasets, knowledge mining, Decision Making, Data Classification, Performance Prediction

Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail

We are motivated by the problem of impromptu or as- you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201

Experimental evaluation of the anti-ulcer activity of the ethanolic extract of grape (Vitis vinifera) seed in wistar albino rats against aspirin plus pylorus ligation induced gastric ulcer model

Background: There is an increased demand for newer safer drugs for the treatment of peptic ulcer disease as its incidence is increasing gradually in view of changing lifestyle and stress. The objective of this study was to evaluate the anti-ulcer activity of ethanol extract of seeds of Vitis vinifera.Methods: The ethanol extract of Vitis vinifera was investigated for its anti-ulcer activity in rats against Aspirin plus Pylorus ligation induced gastric ulcer.The antiulcer activity was assessed by determining and comparing gastric volume, pH, free and total acidity; ulcer number and its inhibition, ulcer severity and ulcer index.Results: A significant antiulcer activity was observed. Pylorus ligation model showed significant (p<0.01) reduction in gastric volume, free acidity and ulcer index as compared to control.Conclusions: This present study indicates that Vitis vinifera seed extract have potential anti-ulcer activity in the model tested

Distinct GDP/GTP bound states of the tandem G-domains of EngA regulate ribosome binding

EngA, a unique GTPase containing a KH-domain preceded by two consecutive G-domains, displays distinct nucleotide binding and hydrolysis activities. So far, Escherichia coli EngA is reported to bind the 50S ribosomal subunit in the guanosine-5′-trihosphate (GTP) bound state. Here, for the first time, using mutations that allow isolating the activities of the two G-domains, GD1 and GD2, we show that apart from 50S, EngA also binds the 30S and 70S subunits. We identify that the key requirement for any EngA–ribosome association is GTP binding to GD2. In this state, EngA displays a weak 50S association, which is further stabilized when GD1 too binds GTP. Exchanging bound GTP with guanosine-5′-diphosphate (GDP), at GD1, results in interactions with 50S, 30S and 70S. Therefore, it appears that GD1 employs GTP hydrolysis as a means to regulate the differential specificity of EngA to either 50S alone or to 50S, 30S and 70S subunits. Furthermore, using constructs lacking either GD1 or both GD1 and GD2, we infer that GD1, when bound to GTP and GDP, adopts distinct conformations to mask or unmask the 30S binding site on EngA. Our results suggest a model where distinct nucleotide-bound states of the two G-domains regulate formation of specific EngA–ribosome complexes