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    Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System
    (2023-01-01)
    Jiriwibhakorn, Somchat
    An adaptive neuro-fuzzy inference system (ANFIS) is a hybrid algorithm composed of fuzzy logic and an artificial neural network. It takes advantage of fuzzy logic and artificial neural networks to solve complex problems. For power transmission, several dynamic parameters are ignored for conventional transient stability assessment due to the complexity of the equations, and due to the long computational time required. Certainly, it is very difficult to do the real-time assessment of large power systems by considering dynamic impacts in detail. In this paper, an approach is required to increase the accuracy of results. Herein, a method, namely ANFIS, was found to overcome the limitations. All significant effects of dynamics can be taken into account; not only the machine model but also the turbine governor model, automatic voltage regulator (AVR) model, and load characteristic model are carefully considered. In addition, the model used for each generator unit is varied to achieve real conditions. The ANFIS output is the critical clearing time (CCT). CCT values are very important to be correctly predicted for the stability of power systems after clearing the faults. When the faults are cleared by opening the circuit breakers within the CCT values, the power systems are still stable. If the faults are cleared after the CCT values, the systems become unstable. The modified IEEE 9-bus and IEEE 39-bus systems are applied in the implementation of this study. The locations of faults and the levels of loads (except the power system topology changes) are varied for each simulation. The results from the ANFIS application indicate that ANFIS (considering the dynamic effects of the machine models, AVR system, turbine governors, and load characteristics) can predict CCT values with high accuracy. Moreover, when ANFIS results are compared to the artificial neural network (ANN) results, which are generally used, it can be seen that ANFIS results are better and take a lower time for training and testing processes than ANN. ANFIS can be adapted, improved, and implemented in real practice.
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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.
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    Online critical clearing time estimation using an adaptive neuro-fuzzy inference system (ANFIS)
    (2015-05-17)
    Phootrakornchai, Witsawa
    ;
    Jiriwibhakorn, Somchat
    This paper describes an approach using an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of online critical clearing time (CCT). The ANFIS can integrate neural networks and fuzzy logic principles, and has a potential to combine the advantages of both in a single framework. In this paper, the ANFIS is applied for the prediction of CCT by varying load levels and fault locations in buses and transmission lines. The IEEE 39-bus system and 9-bus western system coordinating council are tested and implemented in this study. All machines of the IEEE 39-bus system are considered as the classical model without considering any generator's exciters. While three machines in the 9-bus western system coordinating council are considered as detailed models, forth-order differential equation is described for all machines by considering the excitation system controller. CCT values obtained by the time domain simulation method using step-by-step calculation are used as the benchmark. The power world version 17 is used for transient simulation, and the ANFIS is implemented using MATLAB version 2014B. The results obtained from the ANFIS approach are quite satisfied with high accurate solutions and much lower computation time. Finally, the graphical user interface in MATLAB is applied for the online CCT estimation of two test power systems by using appropriate ANFIS models obtained from simulations.