Abstract
For medium and high-powered applications, modular multilevel converters have become the most promising converter application. In this paper, a sliding mode controller based on an RBF neural network is proposed for a modular multilevel converter. The RBF neural network is designed to approximate the uncertainty mathematical model of a modular multilevel converter. The main innovation of the proposed method is that it does not require any model parameters and control parameters during the whole control process. This means that parameter changes caused by the external environment will not influence the controller performances. Finally, by comparing with a conventional PI controller, the simulation proves the feasibility and effectiveness of the proposed control method. In addition, the experimental results show that the grid-side current can become stable immediately while the active power is stabilized after 20 ms when the set value is changed.
Highlights
With the development of power electronic devices, DC transmission devices have changed from two-level converters to three-level converters and to modular multilevel converters (MMC)
Conventional proportional-integral controllers (PI) [5] and proportional-resonant (PR) control [6] have been proposed based on classical control theory and the mathematical model of the MMC
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Summary
With the development of power electronic devices, DC transmission devices have changed from two-level converters to three-level converters and to modular multilevel converters (MMC).the intricated structure of the MMC makes it difficult to control and analyze effectively [1,2]. In order to improve its operating performance, extensive research has been conducted in recent years to address the technical challenges and MMC operation and control [3,4]. For this purpose, conventional proportional-integral controllers (PI) [5] and proportional-resonant (PR) control [6] have been proposed based on classical control theory and the mathematical model of the MMC. Various nonlinear control techniques, such as sliding mode control [7], Lagrange multiplier-based optimal control [8], model-based input–output linearization [9], and developed feedback linearization [10], have been proposed and researched. Conventional nonlinear controller-based techniques rely on a precise mathematical model of the system, which is difficult to achieve in real life
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