Learning-Based Approaches for Voltage Regulation and Control in DC Microgrids with CPL
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This article introduces a novel approach to voltage regulation in a DC/DC boost converter.
The approach leverages two advanced control techniques, including learning-based nonlinear control.
By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based
control techniques, the proposed approach aims to achieve accurate voltage tracking. This is accomplished
by employing the nonlinear distortion observer (NDO) technique, which enables a fast dynamic
response through load power estimation. The process involves training a neural network
using data from the BSC controller. The trained network is subsequently utilized in the voltage
regulation controller. Extensive simulations are conducted to evaluate the performance of the proposed
control strategy, and the results are compared to those obtained using conventional BSC and
model predictive control (MPC) controllers. The simulation results clearly demonstrate the effectiveness
and superiority of the suggested control strategy over BSC and MPC.
Açıklama
Anahtar Kelimeler
ANN, power estimation, BSC, voltage regulation, model predictive control
Kaynak
Sustainability
WoS Q Değeri
Q2
Scopus Q Değeri
Cilt
15
Sayı
15501
Künye
Güngör M, Asker ME. Learning-Based Approaches for Voltage Regulation and Control in DC Microgrids with CPL. Sustainability. 2023; 15(21):15501. https://doi.org/10.3390/su152115501
