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<p>Low-frequency oscillations (LFOs) in interconnected power systems threaten stability, and various damping devices such as Power System Stabilizers (PSSs) often fail to mitigate inter-area modes effectively. High-Voltage Direct Current (HVDC) transmission systems, with independent control of active and reactive power at both converter ends, provide a flexible platform for damping oscillations. This research introduces a hybrid Neuro-Fuzzy Wavelet Controller (NFWC) optimized via the Levenberg–Marquardt Algorithm (LMA) to enhance HVDC system stability. The NFWC combines fuzzy inference with Wavelet Neural Networks (WNNs) to provide a damping current signal to the master control of the HVDC control system. The proposed algorithm utilizes the LMA rather than conventional optimization techniques, thereby avoiding the issue of getting stuck in local minima and effectively damping LFOs. Simulation results on single-machine and multi-machine power systems under varied loading and fault scenarios demonstrate superior transient and steady-state damping performance of the proposed controller. Based on qualitative and quantitative results, it is found that the proposed NFWC significantly improves performance in both transient and steady-state regions.</p>

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Hybrid NeuroFuzzy Wavelet Damping Control Using Levenberg–Marquardt Optimization for HVDC Systems

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Muhammad Abdul Basit,
  • Rabiah Badar,
  • Saad Dilshad,
  • Aun Haider

摘要

Low-frequency oscillations (LFOs) in interconnected power systems threaten stability, and various damping devices such as Power System Stabilizers (PSSs) often fail to mitigate inter-area modes effectively. High-Voltage Direct Current (HVDC) transmission systems, with independent control of active and reactive power at both converter ends, provide a flexible platform for damping oscillations. This research introduces a hybrid Neuro-Fuzzy Wavelet Controller (NFWC) optimized via the Levenberg–Marquardt Algorithm (LMA) to enhance HVDC system stability. The NFWC combines fuzzy inference with Wavelet Neural Networks (WNNs) to provide a damping current signal to the master control of the HVDC control system. The proposed algorithm utilizes the LMA rather than conventional optimization techniques, thereby avoiding the issue of getting stuck in local minima and effectively damping LFOs. Simulation results on single-machine and multi-machine power systems under varied loading and fault scenarios demonstrate superior transient and steady-state damping performance of the proposed controller. Based on qualitative and quantitative results, it is found that the proposed NFWC significantly improves performance in both transient and steady-state regions.