Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Maximum power extraction from photo-voltaic power generator with adaptive MPP tracker

Abstract: Power generation with the help of Photovoltaic (PV) arrays is emphasized increasingly and regarded as an important resource of power energy in the coming years. As the power supplied by PV arrays depends upon the insolation, temperature and array voltage, it is necessary to control the operating point to extract the maximum power from the PV arrays. A number of methods for Maximum Power Point Tracking (MPPT) has been reported in the literature. This paper discusses an adaptive method as well as compares with the conventional fixed step size method, effectively improves the MPPT speed and accuracy simultaneously. An adaptive algorithm and two phase dc-dc Converter is exercised as a MPP tracker. Ripple reduction is possible at input and output side of the converter. Mathematical models of converter are developed using state space averaging technique. The tracking responses of the system operating at the solar array MPP are evaluated. A theoretical analysis of the new algorithm in connection with dc-dc converter is provided and its feasibility is also verified by simulation results.

An Effective Solution for Large Scale Single Machine Total Weighted Tardiness Problem using Lunar Cycle Inspired Artificial Bee Colony Algorithm

Abstract: Single machine total weighted tardiness problem (SMTWTP) is one of the fundamental combinatorial optimization problems. The problem consists of a set of independent jobs with distinct processing times, weights, and due dates to be scheduled on a single machine. The goal of the problem is to minimize the total weighted tardiness. Several swarm intelligence (SI) motivated techniques have been proposed to solve SMTWTP. Still, the solution for large scale SMTWTP instances within a reasonable amount of time is a challenging task. Artificial bee colony (ABC) algorithm is one of the efficient SI based techniques to solve real world optimization problems. This article presents an effective amended ABC based strategy to solve SMTWTP. A local search (LS) approach, influenced from the lunar cycle is proposed and hybridized with ABC to escalate the exploitation capacity of the algorithm. The proposed LS approach is titled as the lunar inspired LS (LLS) approach and the proposed hybridized strategy is known as lunar inspired ABC (LuABC) algorithm. The proposed LuABC algorithm has been applied on 25 large SMTWTP instances of job size 1000. The obtained outcomes prove that the proposed algorithm obtains the optimum solutions for all the considered instances within a reasonable amount of time.

A Machine Learning Algorithm TsF K-NN Based on Automated Data Classification for Securing Mobile Cloud Computing Model

Abstract: Mobile cloud computing (MCC) is fastest growing technology era in which the research society has recently embarked. Today, Mobile data can include financial transactions such as electronic payments, M-wallets and sensitive multimedia contents. The explosive volumes of mobile devices personal data, bring-up more attention to securely data storage rather than consideration on data privacy and confidentiality levels. In this scenario Machine Leaning (ML) brings an important role in the electronic data management. It is always expensive and hard to manage the data manually without adopting machine learning techniques using metadata. Many Machine Learning algorithms have been proposed to comprehend diverse data management issues, yet the forecast of the top secret data and public data in a document is as yet a challenging exploration task. The contribution of this research article is to demonstrate a securing mobile data storage secrecy and privacy in cloud communication framework in terms of automatic data classification using mobile training datasets with help of Training dataset Filtration Key Nearest Neighbor (TsF-KNN) classifier which classifies the data based on the confidentiality level of the record with higher accuracy and powerful timelines as compared to the traditional K-NN algorithms and securing such confidential data category afterwards by applying various existing cryptographic solutions to assuring data privacy and confidentiality levels and simulation results demonstrates that reducing the overall cost and minimize procedural time, increasing system performance and sustainability.

Voltage stability constrained transmission network expansion planning using fast convergent grey wolf optimization algorithm

Abstract: Transmission network expansion planning problem (TNEP) is of utmost importance in power system now a days. In this paper, a voltage stability indicator (L-index) is applied to limit the voltage of buses. Hence TNEP is formulated incorporating this voltage stability feature. Thus formulated TNEP is termed as voltage stability constrained TNEP (VSC-TNEP). TNEP has been solved using various conventional and non-conventional strategies by various researchers in these days. Here, TNEP and VSC-TNEP for IEEE-24 bus test system is solved using grey wolf optimization technique (GWO) and modified GWO. The modified GWO algorithm namely, fast convergent GWO (FCGWO) is validated over 25 standard benchmark test functions and compared with other techniques.