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<p>This paper presents an analysis of a Bi-level Least Mean Square (BLMS) control algorithm for the voltage source inverter (VSI)-based Distribution Static Compensator (d-STATCOM ), envisioned at refining power quality in systems affected by current harmonics. The BLMS algorithm competently extracts the weight essential for reference source current generation from both active and reactive power components of harmonic-rich load currents. Unlike remaining methods, the suggested approach eliminates the need for neural network toolboxes by employing a novel mathematical model for weight update. The algorithm boosts power quality by reducing source current harmonics, regulating voltage, and upgrading the power factor. Furthermore, it allows for reduced sizing of the VSI. The performance of the proposed BLMS algorithm is investigated in MATLAB/SIMULINK and compared to existing adaptive-based least mean square (ALMS) strategies. Finally, real time results using OPAL-RT Simulator are determined to show the adequate performance as per IEEE guidelines.</p>

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Enhancing power quality: bi-level least mean square control algorithm for d-STATCOM

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Ravisankar Kampara,
  • V. Kamaraju,
  • R. Srinivasa Rao

摘要

This paper presents an analysis of a Bi-level Least Mean Square (BLMS) control algorithm for the voltage source inverter (VSI)-based Distribution Static Compensator (d-STATCOM ), envisioned at refining power quality in systems affected by current harmonics. The BLMS algorithm competently extracts the weight essential for reference source current generation from both active and reactive power components of harmonic-rich load currents. Unlike remaining methods, the suggested approach eliminates the need for neural network toolboxes by employing a novel mathematical model for weight update. The algorithm boosts power quality by reducing source current harmonics, regulating voltage, and upgrading the power factor. Furthermore, it allows for reduced sizing of the VSI. The performance of the proposed BLMS algorithm is investigated in MATLAB/SIMULINK and compared to existing adaptive-based least mean square (ALMS) strategies. Finally, real time results using OPAL-RT Simulator are determined to show the adequate performance as per IEEE guidelines.