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.

Comparison of floating photovoltaic plant with solar photovoltaic plant for energy generation at Jodhpur in India

Abstract: Floating Photovoltaic (FPV) power plant on any water body is a Solar Photovoltaic (SPV) plant that is installed on water, instead of land. The water body may be lake, pond, river, canal etc. This paper attempts to calculate and compare energy generation from 1 MW SPV and FPV power plants at Jodhpur city in India using Excel spreadsheet. FPV power plant has numerous advantages over SPV plant. Some of them are: i) Higher electrical energy generation due to low module temperature; ii) Reduction in water evaporation from the water body and thereby conserving valuable potable water; iii) No land requirement for installing the power plant as FPV system floats on the water; and iv) Reduction in the length of power transmission lines as water bodies such as ponds are always close to inhabited areas. However, cost of installation of FPV increases due to need of floating platform on the water body. The solar radiation data for Jodhpur city are taken from National Institute of Wind Energy (NIWE) Wind-Solar data portal. The annual global horizontal radiation for the period of January 01st, 2016 to December 31st, 2016 is estimated as 1.98 MWh/m2. The annual performance ratio and capacity utilization factor for 1 MW SPV are estimated as 79.52% and 19.11% respectively. The annual performance ratio and capacity utilization factor for 1 MW FPV are estimated as 81.49% and 19.58% respectively. 1 MW FPV could save 191.174 million litres of water from being evaporated annually. FPV system compared to SPV system is found to have 2.48% higher energy generation annually with 14.56% reduction in average module temperature.

Sentiment analysis of smart phone product review using SVM classification technique

Abstract: There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shape to the impression of today's generation. Several reviews for a specific product, brand, individual personality, forum sand movies etc. are very helpful in directing the perception of people. Hence the analysts are commenced to create algorithms to automate the classification of distinctive reviews on the basis of their polarities particularly: Positive, Negative and Neutral. This automated classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to apply Support Vector Machine (SVM) classification technique to classify the sentiment sand texts for smart phone product review that analyses different datasets used for classification of sentiments and texts. Furthermore, various data sets have been utilized for training as well as testing and implemented using Support Vector Machine (SVM) to investigate polarity of the ambiguous tweets. The experimental work includes three performance features such as Precision, Recall and F-measure. On the basis of these features, the accuracy of the different products has been computed. The obtained result approves high accuracy as predicted on the basis of smart phone reviews.

Analysis and classification of SQL injection vulnerabilities and attacks on web applications

Abstract: Web applications interact with the back-end database to retrieve data as and when requested by the user Web applications (Like e-commerce, banking, shopping, trading, blogs etc) are the backbone of today's online business industry For activities like paying of bills & merchandize information must be kept safe with these web applications but unfortunately there is no guarantee of integrity and confidentially of information The global exposure of these applications makes them prone to the attacks because of presence of vulnerabilities These security vulnerabilities continue to infect the web applications through injection attacks SQL injection attacks (SQLIA's) are one of the top most threat in database centric web application and SQL injections vulnerabilities(SQLIV's) are the most serious Vulnerability typesSQLIA allows the attacker to gain control over the database of an application resulting in financial fraud, Leak of confidential data, network hacking, deleting database, theft and many more to count In this paper we have discussed the classification of SQL injection attacks and also analysis is done on basis of risk associated with each attack

Comparison of Weibull parameters computation methods and analytical estimation of wind turbine capacity factor using polynomial power curve model: case study of a wind farm

Abstract: Wind speed probability at a site has to be modeled for evaluating the energy generation potential of a wind farm. Analytical computation of wind turbine capacity factor at the planning stage of a wind farm is very crucial. Thus, the comparison of Weibull parameters estimation methods and computation of wind turbine capacity factor are the focus of this paper. Soda wind farm used in this case study is located in the Jaisalmer district of western Rajasthan in India. Modeling of wind speed probability and power curve of wind turbines installed at Soda site were done for analytically estimating the capacity factor of wind turbine. Estimated capacity factors were then compared with the measured values of wind farm for validation of results. Four numerical methods namely graphical, empirical, modified maximum likelihood, and energy pattern factor were used for month-wise Weibull parameters estimation at hub height of 65 m. Power curve of the wind turbine installed at site was modeled using eighth-degree polynomial. Coefficients of polynomial were calculated from the combined use of linear least square method and QR decomposition using Gram-Schmidt orthogonalization method. Results show that the percentage error in annual capacity factor estimation using Weibull parameters estimated from graphical, empirical, modified maximum likelihood, and energy pattern factor methods were +9.98%, −1.59%, −1.22%, and −1.29%, respectively. Annual capacity factor that was estimated using the Weibull parameters calculated from modified maximum likelihood method matched best with the measured values. Graphical method gave the most erroneous results.