Computational and dynamic performance of NN-based control in wind energy systems

Laboratory of Engineering Sciences and Energy Management (LASIME), Ibn Zohr University, National School of Applied Sciences, Agadir, Morroco

^* Corresponding author: b.imodane@uiz.ac.ma

Abstract

In modern power grids, the integration of renewable energy sources such as wind power is crucial to achieving sustainable energy goals. However, the intermittent nature of renewable energy generation creates grid stability issues, particularly with the stabilization of the DC link voltage in systems using Permanent Magnet Synchronous Generators (PMSGs). This paper investigates the effectiveness of a boost converter combined with Neural Network Control (NNC) trained by Sliding Mode Control (SMC) in managing voltage fluctuations. To achieve this, we implement both control strategies on a LAUNCHXL-F28069M DSP board using Processor-In-the-Loop (PIL) techniques, enabling a direct comparison of their dynamic and computational performance. While NNC-based control shows superior adaptability and responsiveness, it consumes more computational resources than SMC, particularly in terms of execution time and power consumption. This study provides an overview of the trade-offs between resource consumption and performance, offering a clearer understanding of control strategies for optimizing renewable energy management in embedded systems.