Dr. B. C. Roy Engineering College

www.bcrec.ac.in
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.

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Mandal B.,Kalyani Government Engineering College | Kumar Roy P.,Dr. B. C. Roy Engineering College
Applied Soft Computing Journal | Year: 2014

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms. © 2014 Elsevier B.V.


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.


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.


Kar S.,National Institute of Technology Durgapur | Das S.,Dr. B. C. Roy Engineering College | Ghosh P.K.,Dr. B. C. Roy Engineering College
Applied Soft Computing Journal | Year: 2014

This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications. © 2013 Elsevier B.V. All rights reserved.


Bhattacharya A.,Dr. B. C. Roy Engineering College | Roy P.K.,Dr. B. C. Roy Engineering College
IET Generation, Transmission and Distribution | Year: 2012

This article presents application of an efficient and reliable heuristic technique inspired by swarm behaviours in nature namely, gravitational search algorithm (GSA) for solution of multi-objective optimal power flow (OPF) problems. GSA is based on the Newton's law of gravity and mass interactions. In the proposed algorithm, the searcher agents are a collection of masses that interact with each other using laws of gravity and motion of Newton. In order to investigate the performance of the proposed scheme, multi-objective OPF problems are solved. A standard 26-bus and IEEE 118-bus systems with three different individual objectives, namely fuel cost minimisation, active power loss minimisation and voltage deviation minimisation, are considered. In multi-objective problem formulation fuel cost and loss; fuel cost and voltage deviation; fuel cost, loss and voltage deviation are minimised simultaneously. Results obtained by GSA are compared with mixed integer particle swarm optimisation, evolutionary programming, genetic algorithm and biogeography-based optimisation. The results show that the new GSA algorithm outperforms the other techniques in terms of convergence speed and global search ability. © 2012 The Institution of Engineering and Technology.


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.


Sultana S.,Dr. B. C. Roy Engineering College | Roy P.K.,Dr. B. C. Roy Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2014

This paper presents teaching learning based optimization (TLBO) approach to minimize power loss and energy cost by optimal placement of capacitors in radial distribution systems. The proposed algorithm is 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. To check the feasibility, the proposed method is applied on standard 22, 69, 85 and 141 bus radial distribution systems. Numerical experiments are included to demonstrate that the proposed TLBO can obtain better quality solution than many existing techniques like genetic algorithm (GA), particle swarm optimization (PSO), direct search algorithm (DSA) and mixed integer linear programming (MILP) approach. © 2013 Elsevier Ltd. All rights reserved.


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.


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.


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

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques. © 2013 Elsevier Ltd. All rights reserved.

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