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Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator.

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Summary

This study introduces a novel Artificial Neural Network (ANN) for smoothing power output from Tidal Stream Generators (TSGs). The ANN improves electrical power quality by managing rotational speed and blade pitch, even with tidal disturbances.

Keywords:
Doubly Fed Induction Generator (DFIG)Maximum Power Point Tracking (MPPT)Tidal Stream Generator (TSG)artificial intelligenceartificial neural networks controlback-to-back converterdata processingpitch regulationpower control

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Ocean Energy Technologies

Background:

  • Tidal Stream Generators (TSGs) offer clean electrical power but face challenges with power quality due to swell and tidal periodicity.
  • Existing methods struggle to consistently smooth power output from TSGs under variable conditions.

Purpose of the Study:

  • To develop and implement a novel Artificial Neural Network (ANN) for enhancing the power smoothing control of Tidal Stream Generators (TSGs).
  • To improve the reliability and quality of electrical power generated from tidal currents.

Main Methods:

  • A novel Artificial Neural Network (ANN) was designed and implemented to control TSG rotational speed and blade pitch angle.
  • The ANN strategy incorporates Maximum Power Point Tracking (MPPT) for optimal energy capture and pitch angle control for system protection.
  • The control system operates in variable speed and power limitation modes, supervised by the ANN.

Main Results:

  • Simulation results demonstrated the effectiveness of the ANN-based control strategies in smoothing the generated power.
  • The implemented methods successfully mitigated power disturbances caused by swell effects and tidal current variations.
  • The system maintained safe operating limits during strong tidal currents through ANN-controlled pitch angle adjustments.

Conclusions:

  • The proposed ANN-based control approach significantly improves power smoothing for Tidal Stream Generators.
  • This methodology enhances the reliability and consistency of electrical power generation from tidal energy sources.
  • The study validates the potential of artificial intelligence in optimizing renewable energy systems for stable power delivery.