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Author: search.filters.author.Jiriwibhakorn, SomchatSubject: search.filters.subject.Adaptive neuro fuzzy inference system

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    Transient staiblity analysis by adaptive neuro fuzzy inference system and sobol sequence
    (2018-07-02)
    Phootrakornchai, Witsawa
    ;
    Jiriwibhakorn, Somchat
    It is known that the time domain is an accurate method that is used for the assessment of transient stability and critical clearing time for any power systems. However, the time domain method normally takes a long time for the calculation due to many differential and non-linear equations, thus it may not be appropriate to apply with the real-time analysis, especially the large power system. We try to find any approaches to minimize the computation time and maximize the accuracy of results as much as possible. This paper therefore proposes a method using adaptive neuro fuzzy inference system and sobol sequence for solving the critical clearing time. The approach proves that it can give us the satisfactory prediction of critical clearing time even for a large power system. The result obtained by adaptive neuro fuzzy inference system are also compared to the result obtained by means of artificial neural network being generally used for the power system analysis.
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    Real-time critical clearing time estimation by considering contingency conditions
    (2018-07-02)
    Phootrakornchai, Witsawa
    ;
    Jiriwibhakorn, Somchat
    This paper presents an approach called adaptive neuro-fuzzy inference system for the transient stability assessment by considering contingency conditions of networks. The contingency condition herein means a case of transmission outage and network configuration change. In addition, in this study all significant dynamic parameters of a power system (e.g. machine models, excitation systems, turbine governors, and load characteristic etc.) are considered for the estimation. We use the critical clearing time for the transient stability index. The 9-bus IEEE is applied for the power dynamic simulation. Finally, this study shows that the adaptive neuro-fuzzy inference system can be applied with the real-time critical clearing time estimation subject to contingency conditions and some parameters affecting the system's dynamic behavior are taken into account.