Optimizing Power System Stability with Advanced Control Techniques in VSC-based HVDC Networks
محورهای موضوعی : مهندسی هوشمند برقNaser Tahaeri 1 , Mohammad Amin Salehi Mishani 2 , Mohammad Moein Yaghoubi 3 , Ehsan Akbari 4
1 - Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran
2 - Department of Electrical Engineering, Azad University, Bushehr Branch, Bushehr, Iran
3 - Energy Management Department, New York Institute of Technology, Vancouver campus, Canada
4 - 4Department of Electrical Engineering, Mazandaran University of Science and Technology, Babol, Iran
کلید واژه: Keywords: Power system dynamic stability, Adaptive Control, Supplementary Damping Controller, Neural Network Control, VSC HVDC,
چکیده مقاله :
The escalating demand for electrical power propels the evolution of power systems from regional to national scales. However, this expansion introduces challenges such as congestion and transmission bottlenecks, compromising system reliability and stability. High Voltage Direct Current (HVDC) systems, particularly those employing Voltage Source Converter (VSC) technology, offer promising solutions due to their unique control capabilities. This paper proposes the utilization of supplementary control alongside VSC-based HVDC to mitigate low-frequency oscillations and enhance dynamic and transient stability in power systems. Through a comprehensive investigation, including linearization of nonlinear power system equations, the efficacy of different input signals for supplementary control is evaluated using techniques like Singular Value Decomposition (SVD), Relative Gain Array (RGA), and Damping Function. The design of a phase compensator as a supplementary controller, employing generator speed deviation as input, is presented based on the linearized model. Additionally, recognizing the limitations of linear controllers in nonlinear systems, an adaptive neural network-based damping controller is proposed to improve dynamical and transient stability. Results demonstrate the effectiveness of the adaptive neural network controller over the phase compensator, particularly in stabilizing the power system and damping oscillations, underscoring the significance of considering nonlinear dynamics in controller design for HVDC systems.
The escalating demand for electrical power propels the evolution of power systems from regional to national scales. However, this expansion introduces challenges such as congestion and transmission bottlenecks, compromising system reliability and stability. High Voltage Direct Current (HVDC) systems, particularly those employing Voltage Source Converter (VSC) technology, offer promising solutions due to their unique control capabilities. This paper proposes the utilization of supplementary control alongside VSC-based HVDC to mitigate low-frequency oscillations and enhance dynamic and transient stability in power systems. Through a comprehensive investigation, including linearization of nonlinear power system equations, the efficacy of different input signals for supplementary control is evaluated using techniques like Singular Value Decomposition (SVD), Relative Gain Array (RGA), and Damping Function. The design of a phase compensator as a supplementary controller, employing generator speed deviation as input, is presented based on the linearized model. Additionally, recognizing the limitations of linear controllers in nonlinear systems, an adaptive neural network-based damping controller is proposed to improve dynamical and transient stability. Results demonstrate the effectiveness of the adaptive neural network controller over the phase compensator, particularly in stabilizing the power system and damping oscillations, underscoring the significance of considering nonlinear dynamics in controller design for HVDC systems.
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