Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.

Wave energy conversion of oscillating water column devices including air compressibility

This paper presents an investigation on air compressibility in the air chamber and its effects on the power conversion of oscillating water column (OWC) devices. As it is well known that for practical OWC plants, their air chambers may be large enough for accommodating significant air compressibility, the “spring effect,” an effect that is frequently and simply regarded to store and release energy during the reciprocating process of a wave cycle. Its insight effects on the device’s performance and power conversion, however, have not been studied in detail. This research will investigate the phenomena with a special focus on the effects of air compressibility on wave energy conversion. Air compressibility itself is a complicated nonlinear process in nature, but it can be linearised for numerical simulations under certain assumptions for frequency domain analysis. In this research work, air compressibility in the OWC devices is first linearised and further coupled with the hydrodynamics of the OWC. It is able to show mathematically that in frequency-domain, air compressibility can increase the spring coefficients of both the water body motion and the device motion (if it is a floating device), and enhance the coupling effects between the water body and the structure. Corresponding to these changes, the OWC performance, the capture power, and the optimised Power Take-off (PTO) damping coefficient in the wave energy conversion can be all modified due to air compressibility. To validate the frequency-domain results and understand the problems better, the more accurate time-domain simulations with fewer assumptions have been used for comparison. It is shown that air compressibility may significantly change the dynamic responses and the capacity of converting wave energy of the OWC devices if the air chamber is very large.