ISWEC Devices on a Wave Farm Handled by a Multi-Agent System

Abstract

This paper aims to contribute to the improvement of the energy extracted from Wave Energy Converters, arrayed in a wave farm, through the implementation of several actions, namely wave extrapolation using a dynamic neural network, wave type identification resorting to a machine learning system, and coordination of the interoperability, communication, and control of all the devices belonging to the wave farm via a Multi-Agent System. The Multi-Agent System was initially developed and subsequently tested through simulations. Simulations used the Inertial Sea Wave Energy Converter, which extracts power from sea waves, using gyroscopic motion, to generate electricity. This device can be originally controlled by proportional-derivative controllers with parameters that change with the sea state. With this Multi-Agent System, ocean waves and corresponding sea states are identified using a machine learning wave classifier method. Information is passed on so that all devices can use optimal control parameters. Therefore, energy extraction by the wave farm is enhanced due to the combination of the Multi-Agent System with automatic learning methods, while its architecture is resilient to communication problems between devices. Simulation results for a case study showed an increase in the average power absorbed, proving the effectiveness of combining a Multi-Agent System with automatic learning methods. Conclusions demonstrated that the proposed system architecture is a viable alternative to the original wave forecasting model. Additionally, this Multi-Agent System can be adapted to wave farms with other Wave Energy Converters and other types of control.