Malaviya National Institute of Technology, Jaipur

www.mnit.ac.in
Jaipur, India

The Malaviya National Institute of Technology Jaipur , formerly known as Malaviya Regional Engineering College Jaipur, is a public engineering institution located in Jaipur, India. It is one of the 30 National Institutes of Technologies and an Institute of National Importance. It was founded in 1963. The institute is fully funded by MHRD, the Government of India. Wikipedia.

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Gupta R.A.,Malaviya National Institute of Technology, Jaipur | Kumar R.,Malaviya National Institute of Technology, Jaipur | Bansal A.K.,Poornima Group of Colleges
Renewable and Sustainable Energy Reviews | Year: 2015

Rising carbon emission or carbon footprint imposes grave concern over the earth's climatic condition, as it results in increasing average global temperature. Renewable energy sources seem to be the favorable solution in this regard. It can reduce the overall energy consumption rate globally. However, the renewable sources are intermittent in nature with very high initial installation price. Off-grid Small Autonomous Hybrid Power Systems (SAHPS) are good alternative for generating electricity locally in remote areas, where the transmission and distribution of electrical energy generated from conventional sources are otherwise complex, difficult and costly. In optimizing SAHPS, weather data over past several years are generally the main input, which include wind speed and solar radiation. The weather resources used in this optimization process have unsystematic variations based on the atmospheric and seasonal phenomenon and it also varies from year to year. While using past data in the analysis of SAHPS performance, it was assumed that the same pattern will be followed in the next year, which in reality is very unlikely to happen. In this paper, we use BBO optimization algorithm for SAHPS optimal component sizing by minimizing the cost of energy. We have also analysed the effect of using forecast weather data instead of past data on the SAHPS performance. ANNs, which are trained with back-propagation training algorithm, are used for wind speed and solar radiation forecasting. A case study was used for demonstrating the performance of BBO optimization algorithm along with forecasting effects. The simulation results clearly showed the advantages of utilizing wind speed and solar radiation forecasting in a SAHPS optimization problem. © 2014 Elsevier Ltd. All rights reserved.


Bansal A.K.,Poornima Group of Colleges | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta R.A.,Malaviya National Institute of Technology, Jaipur
IEEE Transactions on Smart Grid | Year: 2013

In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of Small Autonomous Hybrid Power System (SAHPS) in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of SAHPS, while guaranteeing the availability of energy. Due to the complexity of the SAHPS design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/PV/hydro hybrid energy systems to supply a colony located in the area of Jaipur, Rajasthan (India) during the period of January, 2010 to January 2011. It is clear from the results that the proposed BBO method has excellent convergence property, requires less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain a better solution. © 2013 IEEE.


Nanda S.J.,Malaviya National Institute of Technology, Jaipur | Panda G.,Indian Institute of Technology Bhubaneswar
Swarm and Evolutionary Computation | Year: 2014

The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.


Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta R.A.,Malaviya National Institute of Technology, Jaipur | Bansal A.K.,ISI Technology
Swarm and Evolutionary Computation | Year: 2013

The stand-alone energy system having a photovoltaic (PV) panels or wind turbines have low reliability and high cost as compared with wind/PV hybrid energy system. In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of wind/PV hybrid energy system in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of hybrid energy system, while guaranteeing the availability of energy. A diesel generator is added to ensure uninterrupted power supply due to the intermittent nature of wind and solar resources. Due to the complexity of the hybrid energy system design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/ PV hybrid energy systems to supply a located in the area of Jaipur, Rajasthan (India). Conventional methods require calculation at every single combination of sizing, operation strategy and the data for each variation of component needs to be entered manually and execute separately. Results show that the hybrid energy systems can deliver energy in a stand-alone installation with an acceptable cost. It is clear from the results that the proposed BBO method has excellent convergence property, require less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain the better solution. © 2012 Elsevier B.V.


Verma K.,Malaviya National Institute of Technology, Jaipur | Niazi K.R.,Malaviya National Institute of Technology, Jaipur
International Journal of Electrical Power and Energy Systems | Year: 2012

This paper proposes a supervised learning approach to fast and accurate power system security assessment and contingency analysis. The severity of the contingency is measured by two scalar performance indices (PIs): Voltage-reactive power performance index, PIVQ and line MVA performance index, PIMVA. In this paper, Feed-Forward Artificial Neural Network (FFNN) is employed that uses pattern recognition methodology for security assessment and contingency analysis. A feature selection technique based on the correlation coefficient has been employed to identify the inputs for the FFNN. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system at different loading conditions corresponding to single line outage. The overall accuracy of the test results for unknown patterns highlights the suitability of the approach for online applications at Energy Management Center. © 2011 Elsevier Ltd. All rights reserved.


Kumar R.,Malaviya National Institute of Technology, Jaipur
Swarm and Evolutionary Computation | Year: 2014

The paper presents a new optimization algorithm inspired by group decision-making process of honey bees. The honeybees search for the best nest site among many possible sites taking care of both speed and accuracy. The nest site selection is analogous to finding the optimality in an optimization process. Such similarities between two processes have been used to cultivate a new algorithm by learning from each other. Various experiments have been conducted for better understanding of the algorithm. A comprehensive experimental investigation on the choice of various parameters such as number of bees, starting point for exploration, choice of decision process etc. has been made, discussed and used to formulate a more accurate and robust algorithm. The proposed Directed Bee Colony algorithm (DBC) has been tested on various benchmark optimization problems. To investigate the robustness of DBC, the scalability study is also conducted. The experiments conducted clearly show that the DBC generally outperformed the other approaches. The proposed algorithm has exceptional property of generating a unique optimal solution in comparison to earlier nature inspired approaches and therefore, can be a better option for real-time online optimization problems. © 2014 Elsevier B.V.


Sharma K.K.,Malaviya National Institute of Technology, Jaipur
Signal Processing | Year: 2010

Recently many uncertainty principles involving the product of signal spreads in two linear canonical transform (LCT) and fractional Fourier transform (FRFT) domains have been presented in the literature. In this paper we derive some new equalities/inequalities involving the sum and product of signal spreads in two FRFT domains. Some equalities involving the sum of signal spreads in two LCT domains are also presented. © 2009 Elsevier B.V. All rights reserved.


Sharma K.K.,Malaviya National Institute of Technology, Jaipur
IEEE Signal Processing Letters | Year: 2011

In a vector sampling expansion (VSE), N signals bandlimited to W rad/s in the conventional Fourier domain (CFD) pass through multi-input multi-output (MIMO) linear time-invariant (LTI) systems and produce M ≥ N output bandlimited signals in the CFD. In the uniformly sampled VSE, the outputs of all the M systems are uniformly sampled at the same rate while preserving the total number of samples per second equal to NW/π. In the literature, a necessary condition for perfect signal reconstruction in such a uniformly sampled VSE is discussed which requires M/N to be an integer. It is the purpose of this paper to demonstrate that use of linear canonical transform (LCT) in uniformly sampled VSE allows perfect signal reconstruction even when M/N is not an integer. © 2006 IEEE.


Verma K.,Malaviya National Institute of Technology, Jaipur | Niazi K.R.,Malaviya National Institute of Technology, Jaipur
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper proposes a preventive control of transient stability with generation rescheduling based on coherency obtained using time domain simulations. The transient security dispatch finds a generation configuration with better transient stability behavior. One of the most practiced and an obvious technique of preventive control is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in rescheduling process. Selection of participating generators has been introduced using generator coherency for given contingency. The effectiveness of the proposed methodology is demonstrated on IEEE 145-bus 50-generator system for a three phase fault at different loading conditions with single and multiple line outage cases. © 2012 Elsevier Ltd. All rights reserved.


Khatri R.,Malaviya National Institute of Technology, Jaipur
Energy Reports | Year: 2016

In this paper designing and assessment of a solar PV plant for meeting the energy demand of girl's hostel at MNIT University Jaipur city was analyzed. A solar PV plant was designed with its financial and environmental assessment considering recent market prices. All the aspects related to a solar PV plant were considered for financial feasibility of PV plant near this location. The different financial parameters which affect the financial feasibility of PV plant were considered i.e. discount rate, effective discount rate, rate of escalation of electricity cost, salvage value of the plant etc. The environmental aspect related with the energy generated with PV plant i.e. reduction in carbon emission and carbon credits earned was also considered. Result obtained with the assessment of the proposed plant with different discount rate and current rate of inflation shows that the max IRR 6.85% and NPV of $1,430,834 was obtained with a discount rate of 8% and an inflation rate of 7.23% when no land cost considered and if land cost was considered the maximum IRR was 1.96% and NPV of $630,833. Minimum discounted payback of the plant will be 13.4 years if inflation was considered. © 2016 Published by Elsevier Ltd.

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