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.

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.

Application of the Latching Control System on the Power Performance of a Wave Energy Converter Characterized by Gearbox, Flywheel, and Electrical Generator

The latching control represents an attractive alternative to increase the power absorption of wave energy converters (WECs) by tuning the phase of oscillator velocity to the wave excitation phase. However, increasing the amplitude of motion of the floating body is not the only challenge to obtain a good performance of the WEC. It also depends on the efficiency of the power take-off system (PTO). This study aims to address the actual power performance and operation of a heaving point absorber with a direct mechanical drive PTO system controlled by latching. The PTO characteristics, such as the gear ratio, the flywheel inertia, and the electric generator, are analyzed in the WEC performance. Three cylindrical point absorbers are also considered in the present study. A wave-to-wire model is developed to simulate the coupled hydro-electro-mechanical system in regular waves. The wave energy converter (WEC) performance is analyzed using the potential linear theory but considering the viscous damping effect according to the Morison equation to avoid the overestimated responses of the linear theory near resonance when the latching control system is applied. The latching control system increases the mean power. However, the increase is not significant if the parameters that characterize the WEC provide a considerable mean power. The performance of the proposed mechanical power take-off depends on the gear ratio and flywheel. However, the gear ratio shows a more significant influence than the flywheel inertia. The operating range of the generator and the diameter/draft ratio of the buoy also influence the PTO performance.

Design and optimization of a maglev electromagnetic–triboelectric hybrid energy converter for supplying power to intelligent sensing equipment

An energy converter based on magnetic levitation that combines TENG and EMG ingeniously has been developed. A set of research methods combining theory, experiment and simulation of this type of model has been established.

Energy from the Waves: Integration of a HESS to a Wave Energy Converter in a DC Bus Electrical Architecture to Enhance Grid Power Quality

The need for environmental protection is pushing to a massive introduction of energy production from renewables. Although wind and solar energy present the most mature technologies for energy generation, wave energy has a huge annual energy potential not exploited yet. Indeed, no leading device for wave energy conversion has already been developed. Hence, the future exploitation of wave energy will be strictly related to a specific infrastructure for power distribution and transmission that has to satisfy high requirements to guarantee grid safety and stability, because of the stochastic nature of this source. To this end, an electrical architecture model, based on a common DC bus topology and including a Hybrid Energy Storage System (HESS) composed by Li-ion battery and flywheel coupled to a wave energy converter, is here presented. In detail, this research work wants to investigate the beneficial effects in terms of voltage and current waveforms frequency and transient behavior at the Point of Common Coupling (PCC) introduced by HESS under specific stressful production conditions. Specifically, in the defined simulation scenarios it is demonstrated that the peak value of the voltage wave frequency at the PCC is reduced by 64% to 80% with a faster stabilization in the case of HESS with respect to storage absence, reaching the set value (50 Hz) in a shorter time (by −10% to −42%). Therefore, HESS integration in wave energy converters can strongly reduce safety and stability issues of the main grid relating to intermittent and fluctuating wave production, significantly increasing the tolerance to the expected increasing share of electricity from renewable energy sources.