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.
Highlights
In recent years, the rapid growth in energy consumption and the implementation of the deregulated environment have resulted in the highly interconnected and stressed infrastructure of the power system
The major components of test AC/DC power system comprise the synchronous generators with control, Line Commutated Converters’ (LCC)-High Voltage Direct Current (HVDC) links, transmission lines and transformers
Synchronous machines and control dynamics are expressed by differential equations, while algebraic equations describe the load flow and network model
Summary
The rapid growth in energy consumption and the implementation of the deregulated environment have resulted in the highly interconnected and stressed infrastructure of the power system. Extensive research has been carried out to propose Power Oscillation Damping (POD) controllers applying classical control theory, optimal control schemes, robust control methods and the feedback linearization technique [9,10,11,12,13,14,15,16]. The POD controller realization based on an adaptive feedback linearization becomes more suitable for applications in the highly nonlinear power systems with unknown or uncertain parameters [22]. Model-based methods for identification and control of plants dynamics are well established and widely used, they may not provide satisfactory performance in today’s power system. On-line self-tuning of FBLC coefficients and real-time adaptive neuro-fuzzy identification aspects of ANFFBLC make it an appropriate POD control for HVDC systems. Synchronous machines and control dynamics are expressed by differential equations, while algebraic equations describe the load flow and network model
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
