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

Sheng, Wanan; Lewis, Anthony

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

Files

2011_Sheng_Artificial_Neural_Network_Application_in_Short-Term_Prediction_in_an_Oscillating_Water_Column.pdf(526.16 KB)

Published Version

Date

2010-01

Authors

Sheng, Wanan

Lewis, Anthony

Publisher

The International Society of Offshore and Polar Engineers (ISOPE)

Abstract

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.

Keywords

Artificial neural network , Oscillating water column , Power take-off and contro , Short-Term prediction , Wave energy converter , Power take-off and control , Wave energy

Citation

Sheng, W. and Lewis, A. (2010) ' Artificial neural network application in short-term prediction in an oscillating water column', Proceedings of the 20th International Offshore and Polar Engineering Conference, ISOPE 2010, Beijing; China, 20-25 June. Vol. 1, pp. 774-781.