Performance Analysis of Conjugate Gradient Algorithms Applied to the Neuro-Fuzzy Feedback Linearization-Based Adaptive Control Paradigm for Multiple HVDC Links in AC/DC Power System

Saghir Ahmad and Laiq Khan
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Saghir Ahmad: Department of Electrical Engineering, COMSATS Institute of information Technology, 22060 Abbottabad, Pakistan
Laiq Khan: Department of Electrical Engineering, COMSATS Institute of information Technology, 22060 Abbottabad, Pakistan

Energies, 2017, vol. 10, issue 6, 1-23

Abstract: The existing literature predominantly concentrates on the utilization of the gradient descent algorithm for control systems’ design in power systems for stability enhancement. In this paper, various flavors of the Conjugate Gradient (CG) algorithm have been employed to design the online neuro-fuzzy linearization-based adaptive control strategy for Line Commutated Converters’ (LCC) High Voltage Direct Current (HVDC) links embedded in a multi-machine test power system. The conjugate gradient algorithms are evaluated based on the damping of electro-mechanical oscillatory modes using MATLAB/Simulink. The results validate that all of the conjugate gradient algorithms have outperformed the gradient descent optimization scheme and other conventional and non-conventional control schemes.

Keywords: low-frequency oscillations; HVDC system; adaptive feedback linearization control; adaptive neuro-fuzzy inference system; conjugate gradient algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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