Bender's decomposition algorithm for model predictive control of a modular multi-level converter

In this paper, we consider an optimization problem solving for model predictive control (MPC) of a modular multilevel converter (MMC). An MMC consists of a large number of submodules. The objective of the MPC is to determine the best switching sequences for the submodules in the MMC to track the pha...

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Bibliographic Details
Published in:	2017 North American Power Symposium (NAPS) pp. 1 - 6
Main Authors:	Minyue Ma, Lingling Fan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.09.2017
Subjects:	Bender's decomposition Capacitors Equivalent circuits Linear programming Mathematical model MMC MPC Nonlinear mixed-integer programming problem Optimization Pulse width modulation Switches
Online Access:	Get full text
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Summary:	In this paper, we consider an optimization problem solving for model predictive control (MPC) of a modular multilevel converter (MMC). An MMC consists of a large number of submodules. The objective of the MPC is to determine the best switching sequences for the submodules in the MMC to track the phase current references for T time horizons. The MPC is formulated as a nonlinear mixed-integer programming (MIP) problem with the on/off status of submodules as binary decision variables and MMC dynamic states such as phase currents, circulating currents and submodule capacitor voltages as continuous decision variables. With a large number of submodules and a large number of time horizons, the dimension of the nonlinear MIP problem becomes difficult to handle. Our contribution is to formulate this problem and solve this problem using Bender's decomposition. An example 5-level single-phase MMC is demonstrated for the proposed algorithm.
DOI:	10.1109/NAPS.2017.8107218