ANN-Based Airflow Control for an Oscillating Water Column Using Surface Elevation Measurements

Area of Science:

• Renewable Energy Engineering
• Marine Renewable Energy
• Control Systems

Background:

• Oscillating water column (OWC) wave energy converters are susceptible to power loss from turbine stalling caused by extreme wave events.
• Effective airflow control strategies are crucial for mitigating stalling and optimizing energy extraction in OWC systems.
• Predictive control methods can enhance the operational efficiency of OWC devices.

Purpose of the Study:

• To develop and evaluate an artificial neural network (ANN) based airflow control strategy for OWC plants.
• To enable the OWC system to anticipate incoming waves and proactively adjust turbine airflow velocity.
• To improve the overall power generation efficiency of OWC devices by preventing stalling.

Main Methods:

• Training an artificial neural network (ANN) using real-world surface elevation data of incoming waves.
• Implementing an ANN-based control scheme to predict wave behavior and generate optimal airflow speed references.
• Utilizing an air valve within the OWC capture chamber to regulate airflow velocity based on ANN outputs.
• Conducting comparative analyses between the ANN-controlled OWC system and an uncontrolled OWC system under various sea states.
• Validating the system's performance using measured wave input data and power output from the NEREIDA wave power plant.

Main Results:

• The ANN-based airflow control effectively distinguishes between different wave types, identifying those likely to cause stalling.
• The proposed control strategy successfully adjusted airflow velocity to mitigate the stalling phenomenon.
• Comparative studies demonstrated significant power generation improvements in the ANN-controlled OWC system versus the uncontrolled system.
• Performance validation using NEREIDA wave power plant data confirmed the effectiveness of the ANN control strategy.

Conclusions:

• Artificial neural networks offer a powerful tool for predictive airflow control in OWC systems.
• The developed ANN-based strategy enhances OWC power generation by preventing stalling and optimizing turbine operation.
• This approach represents a significant advancement in improving the efficiency and reliability of wave energy conversion technology.