Learning-based approaches for voltage regulation and control in DC microgrids with CPL
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
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
N/A
Scopus Q Değeri
Cilt
15
Sayı
21
Künye
Güngör, M. ve Asker, M. E. (2023). Learning-based approaches for voltage regulation and control in DC microgrids with CPL. Sustainability (Switzerland), 15(21), 1-14.
