Time filter

Source Type

Durgapur, India

Dr. B. C. Roy Engineering College is a private engineering college in Durgapur, India. It was established on 21 August 2000 with its first batch of students. Wikipedia.

Halder S.,Dr. B. C. Roy Engineering College | Bit S.D.,Bengal Engineering and Science University
Journal of Network and Computer Applications | Year: 2014

Energy is one of the scarcest resources in wireless sensor network (WSN). One fundamental way of conserving energy is judicious deployment of sensor nodes within the network area so that energy flow remains balanced throughout the network. This avoids the problem of occurrence of 'energy holes' and ensures prolonged network lifetime. We have first investigated the problem for enhancing network lifetime using homogeneous sensor nodes. From our observation it is revealed that energy imbalance in WSN occurs due to relaying of data from different parts of the network towards sink. So for improved energy balance instead of using only sensor nodes it is desirable to deploy relay nodes in addition to sensor nodes to manage such imbalance. We have also developed a location-wise pre-determined heterogeneous node deployment strategy based on the principle of energy balancing derived from this analysis, leading to an enhancement of network lifetime. Exhaustive simulation is performed primarily to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing and maintaining coverage. The simulation results also show that our scheme does not compromise with other network performance metrics such as end-to-end delay, packet loss, throughput while achieving the design goal. Finally all the results are compared with two competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics. © 2013 Elsevier Ltd. All rights reserved. Source

Mandal B.,Kalyani Government Engineering College | Roy P.K.,Dr. B. C. Roy Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem. © 2013 Elsevier Ltd. All rights reserved. Source

Roy P.K.,Dr. B. C. Roy Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2013

This article presents a novel teaching learning based optimization (TLBO) to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants. TLBO is a recently developed evolutionary algorithm based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. The algorithm does not require any algorithm-specific parameters which makes the algorithm robust. Numerical results for two sample test systems are presented to demonstrate the capabilities of the proposed TLBO approach to generate optimal solutions of HTS problem. To test the effectiveness, three different cases namely, quadratic cost without prohibited discharge zones; quadratic cost with prohibited discharge zones and valve point loading with prohibited discharge zones are considered. The comparison with other well established techniques demonstrates the superiority of the proposed algorithm. © 2013 Elsevier Ltd. All rights reserved. Source

Mondal S.,Jadavpur University | Bhattacharya A.,Dr. B. C. Roy Engineering College | Nee Dey S.H.,Jadavpur University
International Journal of Electrical Power and Energy Systems | Year: 2013

In this paper an economic emission load dispatch (EELD) problem is solved to minimize the emission of nitrogen oxides (NO X) and fuel cost, considering both thermal generators and wind turbines. The effects of wind power on overall NO X emission are also investigated here. To find the optimum emission dispatch, optimum fuel cost, best compromising emission and fuel cost, a newly developed optimization technique, called Gravitational Search Algorithm (GSA) has been applied. GSA is based on the Newton's law of gravity and mass interactions. In GSA, the searcher agents are collection of masses which interact with each other using laws of gravity and motion of Newton. IEEE 30-bus system having six conventional thermal generators has been considered as test system. Two extra wind turbines are also placed at two weak load bus of the system. Two Weak load buses have been selected based on their L-index value. After placing the wind power sources, those buses have been considered as generator bus. Minimum fuel cost, minimum emission and best compromising solution obtained by GSA are compared with those of biogeography-based optimization (BBO). The results show that the GSA surpasses the other available techniques in terms of solution quality and computational efficiency. © 2012 Elsevier Ltd. All rights reserved. Source

Roy P.K.,Dr. B. C. Roy Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2013

In this article, gravitational search algorithm (GSA) is proposed to solve thermal unit commitment (UC) problem. The objective of UC is to determine the optimal generation of the committed units to meet the load demand and spinning reserve at each time interval, such that the overall cost of generation is minimized, while satisfying different operational constraints. GSA is a new cooperative agents' approach, which is inspired by the observation of the behaviors of all the masses present in the universe due to gravitation force. The proposed method is implemented and tested using MATLAB programming. The tests are carried out using six systems having 10, 20, 40, 60, 80 and 100 units during a scheduling period of 24 h. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, simulated annealing (SA), genetic algorithm (GA), evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO), improved PSO (IPSO), hybrid PSO (HPSO), binary coded PSO (BCPSO), quantum-inspired evolutionary algorithm (QEA), improved quantum-inspired evolutionary algorithm (IQEA), Muller method, quadratic model (QM), iterative linear algorithm (ILA) and binary real coded firefly algorithm (BRCFF). © 2013 Elsevier Ltd. All rights reserved. Source

Discover hidden collaborations