Intelligent control included ANFIS and type-2 fuzzy (T2FLS) controllers grown-up rapidly and these controllers are applied successfully in power system control. Meanwhile, small signal stability problem appear in a largescale power system (LSPS) due to load fluctuation. If this problem persists, and can not be solved, it will develop blackout on the LSPS. How to improve the LSPS stability due to load fluctuation is done in this research by coordinating of PSS based on ANFIS and T2FLS. The ANFIS parameters are obtained automatically by training process. Meanwhile, the T2FLS parameters are determined based on the knowledge that obtained from the ANFIS parameters. Input membership function (MF) of the ANFIS is 5 Gaussian MFs. On the other hand, input MF of the T2FLS is 3 Gaussian MFs. Results show that the T2FLS-PSS is able to maintain the stability by decreasing peak overshoot for rotor speed and angle. The T2FLS-PSS makes the settling time is shorter for rotor speed and angle on local mode oscillation as well as on inter-area oscillation than conventional/ ANFIS-PSS. Also, the T2FLS-PSS gives better performance than the other PSS when tested on single disturbance and multiple disturbances. Keyword: ANFIS INTRODUCTIONController based on artificial intelligent is the research topic interest in recent year. Because the intelligent control has learning ability to improve its performance from the environment where the controller is applied. The spreading of intelligent control exists on some fields such as: Electrical and electronic engineering, and computer science such as: Fuzzy controller is used to enhance photovoltaic generator power quality by maintaining the MPPT tracking on low voltage side of DC/DC boost converter. The MPPT-fuzzy controller is able to maintain voltage profile and current total harmonic distortion (THD) [1]. The performance of fuzzy controller is compared to the PI controller on two inverter fed to control six-phase permanent magnet synchronous machine (PMSM) operated as a motor. The simulation results show that the fuzzy controller are more robust, quick response on high starting torque and more effective the PI controller [2]. Support vector machine (SVM) method is used to classify a large-scale power system transient stability. The SVM method gives a better result than multi-layer perceptron-neural network (MLP-NN)

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Abstract

Current control scheme is commonly used in high voltage direct current (HVDC) to transmit power delivery. This scheme is done by adjusting trigger angle to regulate direct current (DC) in thyristor devices. The adaptive neuro-fuzzy inference system (ANFIS) control is widely applied for start and fault operation. But, solution for transient response of DC current in HVDC system is not clearly studied before. In this paper, supplementary control (SC) based on ANFIS is proposed to improve the transient response of the current. The SC control is designed by learning-processes and SC parameters are obtained by data-training automatically. For current reference at 1.05 pu and up-ramp at 20 pu/s, maximum overshoot is achieved at 5.12% and 7.72% for the SC and proportional integral controller (PIC), respectively. When the up-ramp is increased to 28 pu/s, the maximum overshoot is achieved at 10.01% for the SC. While, the peak overshoot for the PIC is 14.28%.

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