Compartir
Stability Enhancement Methods of Inverters Based on Lyapunov Function, Predictive Control, and Reinforcement Learning (en Inglés)
Xin Zhang; Jinsong He; Hao Ma; Zhixun Ma; Xiaohai Ge (Autor)
·
Springer
· Tapa Blanda
Stability Enhancement Methods of Inverters Based on Lyapunov Function, Predictive Control, and Reinforcement Learning (en Inglés) - Xin Zhang; Jinsong He; Hao Ma; Zhixun Ma; Xiaohai Ge
$ 236.290
$ 328.180
Ahorras: $ 91.890
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis Listas
Origen: Estados Unidos
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Lunes 15 de Julio y el
Lunes 22 de Julio.
Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.
Reseña del libro "Stability Enhancement Methods of Inverters Based on Lyapunov Function, Predictive Control, and Reinforcement Learning (en Inglés)"
This book introduces a family of large-signal stability-based control methods for different power inverters (grid-connected inverter, standalone inverter, single-phase inverter, and three-phase inverter) in practical applications. Power inverters have stability issues, which include the inverter's own instability as well as the inverter's instability in relation to the other power electronic devices in the system (i.e., weak grid and the EMI filter). Most of the stability analyses and solutions are based on small-signal stability technology. Unfortunately, in actuality, the majority of practical instability concerns in power inverter systems are large-signal stability problems, which, when compared to small-signal stability problems, can cause substantial damage to electrical equipment. As a result, researchers must conduct a comprehensive investigation of the large-signal stability challenge and solutions for power inverters. This book can be used as a reference for researchers, power inverters manufacturers, and end-users. As a result, the book will not become obsolete in the near future, regardless of technology advancements.