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A real-time and closed-loop control algorithm for cascaded multilevel inverter based on artificial neural network.

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A novel artificial neural network (ANN) control algorithm offers real-time management for cascaded H-bridges (CHB) converters. This method significantly reduces total harmonic distortion (THD) and computation time, ensuring stable performance under disturbances.

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Area of Science:

  • Electrical Engineering
  • Power Electronics
  • Control Systems

Background:

  • Cascaded H-bridges (CHB) converters are crucial in power electronics.
  • Staircase modulation strategies require efficient real-time control.
  • Minimizing total harmonic distortion (THD) is essential for power quality.

Purpose of the Study:

  • To develop a real-time, closed-loop control algorithm for three-phase CHB converters.
  • To reduce computation time and memory usage for staircase modulation.
  • To improve THD performance across a wider modulation index range.

Main Methods:

  • Offline optimization of switching angles using hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) to minimize THD.
  • Training an artificial neural network (ANN) with optimal switching angles for real-time angle generation.
  • Embedding the ANN into a closed-loop control system for CHB converters with variable DC sources.

Main Results:

  • The proposed ANN-based algorithm achieves real-time optimal switching angle generation.
  • It results in lower THD (<5%) and reduced calculation time compared to previous methods.
  • The closed-loop system effectively stabilizes load voltage and minimizes line current THD during DC source or load disturbances.

Conclusions:

  • The developed ANN-based real-time control algorithm is effective for CHB converters.
  • It offers superior performance in terms of THD and computational efficiency.
  • Experimental verification confirms the algorithm's correctness and practical applicability.