Indirect adaptive neural network control for constant power conversion in wave energy system

Abstract

The conversion of ocean wave energy into electrical energy occurs near beaches and is important for the design and implementation of wave energy conversion (WEC) systems. However, its generation depends on environmental conditions, which complicates the design and control of the devices. This work presents an approach to indirect adaptive control based on artificial neural networks to detect wave conditions for the proper functioning of WEC structures. The method involves generating a constant output voltage using a voltage boost converter and a direct current-alternating current (DC-AC) converter. Maintaining a constant output power despite variations in wave conditions to generate a voltage of 24 V with a current of 2 A is the primary proposal for the control design. The mechanical design integrates a rack and pinion system and a pulley transmission that connects a floating device to an electric generator. The implementation of control is carried out on an Arduino platform. The control system was implemented on an Arduino platform, occupying 48% of the available memory, with a convergence time of 4.29 ms, a mean squared error (MSE) of 0.13715, and a root mean squared error (RMSE) of 0.37034. These low values indicate that the proposed control system has greater accuracy. The experimental results validate the proposed control system, which reduces energy conversion errors and achieves greater efficiency.