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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. Source


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. Source


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. Source


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. Source


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. Source

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