SET Proceedings 2978-4387 Caravel Press 10.65582/set.2022.115 2022.115 Control of three-phase grid-connected microgrids using multi-layer artificial neural networks BaraghthaYusra yasora.barghouthi@gmail.com LiuFang csuliufang@csu.edu.cn Al-HalabiAshraf aalhalabi@birzeit.edu School of Automation, Central South University, Changsha 410083, China Birzeit University, Palestine 16022023 19 3 978-0-85358-351-6 222 232 16 February 2023 © 2022 Copyright WSSET 2023

A microgrid can acquire a better quality voltage profile, high power performance, and fast connection or disconnection of a distribution power source to the microgrid by using a neural network control strategy. In this paper, we propose a multilayer artificial neural network perceptron based on the Maximum Power Point Tracking (MPPT) algorithm to improve energy performance and reduce power losses in the power distribution system among microgrids by imposing a short distance between them. The system model was built out in the MATLAB/SIMULINK environment.

 multi-layer artificial neural network (MANN) maximum power point tracking (MPPT) microgrid photovoltaic system wind turbine 16 to 18 August 2022 19th International Conference on Sustainable Energy Technologies SET2022 19 Istanbul, Turkiye