Deep Learning Prediction for Rotational Speed of Turbine in Oscillating Water Column-Type Wave Energy Converter

This study uses deep learning algorithms to predict the rotational speed of the turbine generator in an oscillating water column-type wave energy converter (OWC-WEC). The effective control and operation of OWC-WECs remain a challenge due to the variation in the input wave energy and the significantly high peak-to-average power ratio. Therefore, the rated power control of OWC-WECs is essential for increasing the operating time and power output. The existing rated power control method is based on the instantaneous rotational speed of the turbine generator. However, due to physical limitations, such as the valve operating time, a more refined rated power control method is required. Therefore, we propose a method that applies a deep learning algorithm. Our method predicts the instantaneous rotational speed of the turbine generator and the rated power control is performed based on the prediction. This enables precise control through the operation of the high-speed safety valve before the energy input exceeds the rated value. The prediction performances for various algorithms, such as a multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and convolutional neural network (CNN), are compared. In addition, the prediction performance of each algorithm as a function of the input datasets is investigated using various error evaluation methods. For the training datasets, the operation data from an OWC-WEC west of Jeju in South Korea is used. The analysis demonstrates that LSTM exhibits the most accurate prediction of the instantaneous rotational speed of a turbine generator and CNN has visible advantages when the data correlation is low.

Comparative study of offshore spar-buoy oscillating water column dynamic models for captured power estimation

The prompt estimation of power and geometrical aspects enables faster and more accurate financial assessment of wave energy converters to be deployed. This may lead to better commercialisation of wave energy technologies, as they require location-based customisation, unlike the mature wind energy technologies with developed benchmark. The adopted approach provides simple and efficient modelling tool allowing the study of the system from different perspective. The aim of this study is to select the optimum dynamic model to predict the captured power of a spar-buoy Oscillating Water Column (OWC) wave energy converter. Four dynamic models were developed to predict the system dynamics and results were validated experimentally. In-depth investigations on the effect of the mass and damping ratios of the oscillating bodies on the accuracy of the adopted models were performed. Such investigations included the proposed one-way coupling model and three two-degree of freedom models and three reduced-scale models, in addition to analytical and numerical solutions. Pneumatic power was calculated for the reduced-scale model where orifices’ covers simulated the power take-off mechanism damping experimentally. Analysis and comparisons between the adopted models are finally provided.

Development of a Computational Model for Investigation of and Oscillating Water Column Device with a Savonius Turbine

The present work aims to develop a computational model investigating turbulent flows in a problem that simulates an oscillating water column device (OWC) considering a Savonius turbine in the air duct region. Incompressible, two-dimensional, unsteady, and turbulent flows were considered for three different configurations: (1) free turbine inserted in a long and large channel for verification/validation of the model, (2) an enclosure domain that mimics an OWC device with a constant velocity at its inlet, and (3) the same domain as that in Case 2 with sinusoidal velocity imposed at the inlet. A dynamic rotational mesh in the turbine region was imposed. Time-averaged equations of the conservation of mass and balance of momentum with the k–ω Shear Stress Transport (SST) model for turbulence closure were solved with the finite volume method. The developed model led to promising results, predicting similar time–spatial-averaged power coefficients (CP¯) as those obtained in the literature for different magnitudes of the tip speed ratio (0.75 ≤ λ ≤ 2.00). The simulation of the enclosure domain increased CP¯ for all studied values of λ in comparison with a free turbine (Case 1). The imposition of sinusoidal velocity (Case 3) led to a similar performance as that obtained for constant velocity (Case 2).

Overview on Oscillating Water Column Devices

Oscillating-water-column (OWC) converters, of fixed structure or floating, are an important class of wave energy devices. A large part of wave energy converter prototypes deployed so far into the sea are of OWC type. The paper presents a review of recent advances in OWC technology, including sea-tested prototypes and plants, new concepts, air turbines, model testing techniques and control.

Oscillating water column plant

This article is describing the way of construction and operation of an oscillating water column system in order to recover as much as possible from the waves energy. The oscillating water column plant is used for the production of electrical energy by tidal currents, and it is currently the most widespread and economical method for the conversion of wave motion. The environmental impact of these infrastructures remains very low: no emissions of gas or any waste during their operation. In addition, the swell is a formidable source of energy.

Electric propulsion of ships and its advantages for anchor lifting towing vessels

This article is describing the way of construction and operation of an oscillating water column system in order to recover as much as possible from the waves energy. The oscillating water column plant is used for the production of electrical energy by tidal currents, and it is currently the most widespread and economical method for the conversion of wave motion. The environmental impact of these infrastructures remains very low: no emissions of gas or any waste during their operation. In addition, the swell is a formidable source of energy.