Hexagonal zoning deadbeat model predictive control of induction motor

IET ELECTRIC POWER APPLICATIONS(2024)

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摘要
Deadbeat model predictive control is known as a precise control method in motor control applications. Recently, finite set deadbeat control is applied to power electronics applications. A voltage-based cost function is used to select the closest voltage vector to the reference vector by examining all possible states in the cost function. This method needs a high amount of computation when it is applied to multilevel inverters or virtual voltage vectors due to a considerable increase in the number of candidates. Some studies have been done to reduce the number of candidate voltage vectors and computation burden. The methodology in these methods is based on finding the adjacent area and applying the vertex vectors to the cost function. So, these strategies are multi-step and complex. The hexagonal zoning deadbeat model predictive control is proposed and introduced. The basic idea of the proposed method is that each zone should contain only one vector. In this way, the area identification is equal to finding the optimum voltage selection. Hexagonal zoning is used, which simplifies the process of identifying the area. A simple area identification method is developed based on the reference voltage phase angle. The proposed method can be applied to different inverters with different spatial vector diagrams. Furthermore, the synthesis of the virtual vector is manipulated to reduce the torque and flux ripple. The proposed method is experimentally evaluated by applications to the virtual voltage switching of a two-level inverter for the induction motor drive in this research. The authors work on the finite control set deadbeat predictive control. The proposed method simplifies and optimises the switching synthesis of the reference voltage vector. The hexagonal zoning technique introduced eliminates the need for the voltage-based cost function.image
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关键词
AC motor drives,predictive control,switching systems (control)
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