A robust system stabilizer configuration using artificial neural network based on linear optimal control (student paper competition)

Youssef, Mohamed; Jain, Praveen; Mohamed, E. A.

doi:10.1109/ccece.2003.1226460

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…Delve has confirmed that ANNs have a extensive number of application in the power engineering due to many virtues [29,30] such as capability of synthesize multifaceted and discernible planning, briskness due to parallel mechanism, robustness and fault forbearance. In this article, ANFIS controller in MATLAB/Simulink, which is an advance adaptive control configuration of ANN, is employed.…”

Section: Hnf Approachmentioning

confidence: 88%

“…Adaptive controllers with self-adjusting gain settings have been proposed for LFC [4,8,[13][14][15]. There has also been considerable research work attempting to propose better AGC systems based on modern control theory [20], neural network [2,3,5,6,29] fuzzy system theory [4] and reinforcement learning [7]. Recent study confirms that ANFIS approach has also been applied to hydrothermal system [16,23,28].…”

Section: Introductionmentioning

confidence: 95%

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Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system

Shree

Kamaraj²

2016

International Journal of Electrical Power & Energy Systems

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Section: Hnf Approachmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 95%

Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system

Shree

Kamaraj²

2016

International Journal of Electrical Power & Energy Systems

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“…This structure of the ANN has an input layer, an output layer, and one hidden layer. Research has proved that ANNs have a wide number of applications in power engineering due to many advantages [9,10]:…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis) Approachmentioning

confidence: 99%

“…The applications include economical load dispatching, Power System Stabilizers (PSS), etc. The results have shown that ANNs have great potential in improving power system online and off-line applications [9][10][11][12]. The proposed controller has been applied and tested under different disturbances for a weakly connected single-machine infinite-bus power system.…”

Section: Introductionmentioning

confidence: 99%

ANFIS approach for SSSC controller design for the improvement of transient stability performance

Khuntia

Panda

2013

Mathematical and Computer Modelling

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Adaptive neuro-fuzzy inference system (ANFIS) Flexible ac transmission system (FACTS) Single machine infinite bus (SMIB) Static synchronous series compensator (SSSC) Transient stability a b s t r a c t In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for the improvement of transient stability. The proposed ANFIS controller combines the advantages of a fuzzy controller as well as the quick response and adaptability nature of an ANN. The ANFIS structures were trained using the generated database by the fuzzy controller of the SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and changes in operating conditions. Further, the simulation results obtained are compared with the conventional lead-lag controller under various disturbances, namely three-phase unbalanced faults and other unbalanced faults to show the effectiveness and robustness of the proposed approach for SSSC.

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“…The applications include economical load dispatching, power system stabilizers (PSS), etc. The results have shown that ANNs have great potential in improving power system online and off-line applications [17][18][19][20]. The proposed controller has been applied and tested under different disturbances for a multi-machine power system.…”

Section: Introductionmentioning

confidence: 97%

Simulation study of a SSSC-based neuro-fuzzy controller for improvement of transient stability in a three-machine power system

Khuntia

2012

2012 IEEE Energytech

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In this article, computationally simple and accurate expert system, i.e., neuro-fuzzy system based on the artificial neural network (ANN) is applied to design a static synchronous series compensator (SSSC)-based controller for improvement of transient stability in a three-machine power system. The proposed neuro-fuzzy controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. The neuro-fuzzy structures were trained using the generated database of fuzzy controller for SSSC. The results prove that the proposed SSSC-based neuro-fuzzy controller is found to be robust to fault location and change in operating conditions. A thorough comparison with the conventional leadlag controller is carried out, taking into account the results of previous publications. The SSSC-based neuro-fuzzy controller output provides promising results in terms of accuracy and computation time. Finally, conclusions are duly drawn.

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A robust system stabilizer configuration using artificial neural network based on linear optimal control (student paper competition)

Cited by 8 publications

References 6 publications

Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system

Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system

ANFIS approach for SSSC controller design for the improvement of transient stability performance

Simulation study of a SSSC-based neuro-fuzzy controller for improvement of transient stability in a three-machine power system

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