Jiriwibhakorn, Somchat

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Jiriwibhakorn, Somchat
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Jiriwibhakorn, S.
Jiriwibhakorn, Somchart
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somchat.ji@kmitl.ac.th

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    Optimal power flow problem solved by using Distributed Sobol particle swarm optimization
    (2010-07-30)
    Wannakarn, P.
    ;
    Khamsawang, S.
    ;
    Pothiya, S.
    ;
    Jiriwibhakorn, S.
    The Distributed Sobol particle swarm optimization (DSPSO) algorithm was studies for solving optimal power flow problem (OPF), in this paper. In the proposed method, swarm size of the particles is separated in multi-groups and searching procedure is divided according with the swarm group. Reducing search space and high cost elimination are concluded in the DSPSO. The DSPSO was tested for solving two sizes of the OPF problem, six bus test system and IEEE-30 bus test system respectively. The numerical results obtain from the DSPSO were compare with many optimization methods, namely bee colony algorithm (BA), differential evolution algorithm (DE), genetic algorithm (GA), particle swarm optimization (PSO) and tabu search algorithm (TSA). The results show that the proposed method had faster convergence and better solution than the rest methods.
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    Solving the economic dispatch problem by using differential evolution
    (2009-10-22)
    Khamsawang, S.
    ;
    Wannakarn, P.
    ;
    Pothiya, S.
    ;
    Jiriwibhakorn, S.
    This paper proposes an application of the differential evolution (DE) algorithm for solving the economic dispatch problem (ED). Furthermore, the regenerating population procedure added to the conventional DE in order to improve escaping the local minimum solution. To test performance of DE algorithm, three thermal generating units with valve-point loading effects is used for testing. Moreover, investigating the DE parameters is presented. The simulation results show that the DE algorithm, which had been adjusted parameters, is better convergent time than other optimization methods. ©2009 IEEE.
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    DSPSO-TSA for economic dispatch problem with nonsmooth and noncontinuous cost functions
    (2010-02-01)
    Khamsawang, S.
    ;
    Jiriwibhakorn, S.
    This paper proposes a new approach based on particle swarm optimization (PSO) and tabu search algorithm (TSA). This proposed approach is called distributed Sobol PSO and TSA (DSPSO-TSA). In order to improve the convergence characteristic and solution quality of searching process, three mechanisms had been presented. Firstly, the Sobol sequence is applied to generate an inertia factor instead of the existing process. Secondly, a distributed process is used so as to reach the global solution rapidly. The search process is divided to multi-stages and used a short-term memory for recognition the best search history. Finally, to guarantee the global solution, TSA had been activated to adjust the obtained solution of DSPSO algorithm. To show its effectiveness, the proposed DSPSO-TSA is applied to test four case studies of economic dispatch (ED) problem considering nonsmooth and noncontinuous fuel cost functions of generating units. The simulation results obtained from DSPSO-TSA are compared with conventional approaches such as genetic algorithm (GA), TSA, PSO, and others in literatures. The comparison results show that the efficiency of proposed approach can reach higher quality solution and faster computational time than the conventional methods. © 2009 Elsevier Ltd. All rights reserved.
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    Hybrid PSO-DE for solving the economic dispatch problem with generator constraints
    (2010-05-28)
    Khamsawang, S.
    ;
    Wannakarn, P.
    ;
    Jiriwibhakorn, S.
    This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called hybrid particle swarm optimization - differential evolution (PSO-DE). The mutation operators are activated if velocity values of PSO nearly to zero or violated from the boundaries. Four scenarios of mutation operators are implemented for PSO-DE. The simulation results of all scenarios of the PSO-DE outperform over the PSO and other existing approaches which appeared in literatures. ©2010 IEEE.