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<p>This paper proposes an enhanced Battery Energy Storage Damping Controller (BESDC) as a novel intelligent solution for mitigating Sub-Synchronous Resonance (SSR) in Fixed Series Compensated Transmission Lines (FSCTL) connected to Doubly Fed Induction Generator (DFIG)-based wind power system. Addressing the growing complexity and stability challenges of modern power systems, the controller integrates a Supplementary Damping Signal (SDS) into the d–q axis control structure of the BESDC. This SDS utilizes real-time angular speed deviation data acquired via a Wide Area Measurement System (WAMS), enabling system-wide observability and dynamic response. To ensure robust damping across varying operating conditions, an Upgraded Particle Swarm Optimization (UPSO) algorithm, guided by eigenvalue sensitivity analysis, is employed to optimally tune the controller gain. The study accounts for the intermittency of wind speeds and variability in FSCTL levels, representing practical grid scenarios. Time-domain simulations conducted in MATLAB/Simulink demonstrate that the proposed intelligent BESDC consistently stabilizes critical modes at wind speeds of 6&#xa0;m/s, 7&#xa0;m/s, 9&#xa0;m/s, and 11&#xa0;m/s and compensation levels of 40%, 50%, and 60%. Compared to conventional BESDC designs, the proposed framework achieves superior damping performance and improved system resilience. These results underscore the potential of intelligent, optimization-based damping strategies in developing stable, future-ready electrical infrastructures.</p>

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Intelligent Control for Sub-Synchronous Resonance Mitigation: A UPSO-Optimized Battery Storage Approach for Future Wind Power Systems

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Neevatika Verma,
  • Narendra Kumar

摘要

This paper proposes an enhanced Battery Energy Storage Damping Controller (BESDC) as a novel intelligent solution for mitigating Sub-Synchronous Resonance (SSR) in Fixed Series Compensated Transmission Lines (FSCTL) connected to Doubly Fed Induction Generator (DFIG)-based wind power system. Addressing the growing complexity and stability challenges of modern power systems, the controller integrates a Supplementary Damping Signal (SDS) into the d–q axis control structure of the BESDC. This SDS utilizes real-time angular speed deviation data acquired via a Wide Area Measurement System (WAMS), enabling system-wide observability and dynamic response. To ensure robust damping across varying operating conditions, an Upgraded Particle Swarm Optimization (UPSO) algorithm, guided by eigenvalue sensitivity analysis, is employed to optimally tune the controller gain. The study accounts for the intermittency of wind speeds and variability in FSCTL levels, representing practical grid scenarios. Time-domain simulations conducted in MATLAB/Simulink demonstrate that the proposed intelligent BESDC consistently stabilizes critical modes at wind speeds of 6 m/s, 7 m/s, 9 m/s, and 11 m/s and compensation levels of 40%, 50%, and 60%. Compared to conventional BESDC designs, the proposed framework achieves superior damping performance and improved system resilience. These results underscore the potential of intelligent, optimization-based damping strategies in developing stable, future-ready electrical infrastructures.