Stability Enhancement Methods Of Inverters Based On Lyapunov Function Predictive Control And Reinforcement Learning
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Stability Enhancement Methods of Inverters Based on Lyapunov Function, Predictive Control, and Reinforcement Learning
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.
Model Predictive Control in the Process Industry
Author: Eduardo F. Camacho
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.