Asansol Engineering College

Āsansol, India

Asansol Engineering College

Āsansol, India

Time filter

Source Type

Shaw B.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
IET Generation, Transmission and Distribution | Year: 2011

This study presents a seeker optimisation algorithm (SOA) for the solution of the constrained economic load dispatch (ELD) problems in different power systems considering various non-linear characteristics of generators. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimisation. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. A comparison of simulation results reveals the optimisation efficacy of the algorithm over the prevailing optimisation techniques for the solution of the multimodal, non-differentiable, highly non-linear and constrained ELD problems. © 2011 The Institution of Engineering and Technology.


Chatterjee A.,Asansol Engineering College | Ghoshal S.P.,National Institute of Technology Durgapur | Mukherjee V.,Indian School of Mines
International Journal of Electrical Power and Energy Systems | Year: 2011

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables. © 2010 Elsevier Ltd. All rights reserved.


Chatterjee A.,Asansol Engineering College | Ghoshal S.P.,National Institute of Technology Durgapur | Mukherjee V.,Indian School of Mines
International Journal of Electrical Power and Energy Systems | Year: 2012

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. Harmony search (HS) is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper proposes a novel approach to accelerate the HS algorithm. The proposed opposition-based HS of the present work employs opposition-based learning for harmony memory initialization and also for the generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the HS. The potential of the proposed algorithm, presented in this paper, is assessed by means of an extensive comparative study of the solution obtained for four standard combined economic and emission dispatch problems of power systems. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Both the near-optimality of the solution and the convergence speed of the proposed algorithm are found to be promising. © 2012 Elsevier Ltd. All rights reserved.


Shaw B.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
International Journal of Electrical Power and Energy Systems | Year: 2012

Gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses. In GSA, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. This paper proposes a novel algorithm to accelerate the performance of the GSA. The proposed opposition-based GSA (OGSA) of the present work employs opposition-based learning for population initialization and also for generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the GSA. For the experimental verification of the proposed algorithm, a comprehensive set of 23 complex benchmark test functions including a wide range of dimensions is employed. Additionally, four standard power systems problems of combined economic and emission dispatch (CEED) are solved by the OGSA to establish the optimizing efficacy of the proposed algorithm. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Both the near-optimality of the solution and the convergence speed of the proposed algorithm are promising. © 2011 Elsevier Ltd. All rights reserved.2.


Shaw B.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
Expert Systems with Applications | Year: 2012

Seeker optimization algorithm (SOA), a novel heuristic population-based search algorithm, is utilized in this paper to solve different economic dispatch (ED) problems of thermal power units. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the algorithm has been tested on four different small, as well as, large scale test power systems to solve the ED problems. The outcome of the present work is to establish the SOA as a promising alternative approach to solve the ED problems in practical power systems. Both the near-optimality of the solution and the convergence speed of the algorithm are promising. The results obtained are compared with those published in the recent literatures. © 2011 Published by Elsevier Ltd.


Shaw B.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
International Journal of Electrical Power and Energy Systems | Year: 2014

Gravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57- and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.


Banerjee A.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
International Journal of Electrical Power and Energy Systems | Year: 2014

In this paper, an isolated wind-diesel hybrid power system model is considered for its on-line reactive power compensation. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between the reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, variable source of reactive power such as static VAR compensator (SVC) is used. The different tunable parameters of the studied hybrid power system model are optimized by a novel opposition-based gravitational search algorithm (OGSA). Gravitational search algorithm (GSA) is based on the law of gravity and the interaction between the masses. In GSA, the searcher agents are a collection of masses and their interactions are based on the Newtonian laws of gravity and motion. To further improve the optimization performance of the GSA, opposition-based learning is employed for population initialization and also for generation jumping. The performance analysis of a Sugeno fuzzy logic (SFL) based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. Time-domain simulation of the investigated power system model reveals that the proposed OGSA-SFL yields on-line, off-nominal optimal SVC parameters resulting in on-line optimal terminal voltage response.©2013 Elsevier Ltd. All rights reserved.


Chatterjee A.,Asansol Engineering College | Ghoshal S.P.,National Institute of Technology Durgapur | Mukherjee V.,Indian School of Mines
International Journal of Bio-Inspired Computation | Year: 2012

Gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising. Copyright © 2012 Inderscience Enterprises Ltd.


Banerjee A.,Asansol Engineering College | Mukherjee V.,Indian School of Mines | Ghoshal S.P.,National Institute of Technology Durgapur
Swarm and Evolutionary Computation | Year: 2013

Seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, effectiveness of the SOA has been tested for optimized reactive power control of an isolated wind-diesel hybrid power system model. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for the purpose of power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, a variable source of reactive power such as static VAR compensator (SVC) is used. The SG is equipped with IEEE type-I excitation system and dual input power system stabilizer (PSS) like IEEE-PSS3B. The performance analysis of a Takagi-Sugeno fuzzy logic (TSFL)-based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. In time-domain simulation of the investigated power system model, the proposed SOA-TSFL yields on-line, off-nominal coordinated optimal SVC and PSS parameters resulting in on-line optimal reactive power control and terminal voltage response. The performance of the proposed controller, with the influence of signal transmission delay, has also been investigated. © 2013 Elsevier B.V.


Chatterjee S.K.,Asansol Engineering College
IEEE Transactions on Consumer Electronics | Year: 2012

An efficient architecture that implements motion estimation based on a combination of diamond search algorithm and weighted constrained one-bit transformation is presented. The component of any motion estimation hardware that deals with the memory access consumes considerable power. This paper reports efforts at exploiting the overlap of search data among various search locations to reduce the number of memory accesses. The proposed architecture can reduce total power consumption by up to 23% compared to a recently reported architecture. Moreover, only 9 processing elements are used in this architecture, as against 16 processing elements used in conventional one bit transformation based motion estimation hardware. This in turn substantially reduces the total area of the architecture. As the proposed architecture is area efficient in addition to consuming relatively less power, its potential application lies in portable consumer video playback systems typically operated by batteries. © 1975-2011 IEEE.

Loading Asansol Engineering College collaborators
Loading Asansol Engineering College collaborators