Capacitor Voltage Estimation Scheme with Reduced Number of Sensors for Modular Multilevel Converters


This paper presents a new method to measure the voltage across the submodule (SM) capacitors in a modular multilevel converter (MMC). The proposed technique requires only one voltage sensor per arm. This reduces the number of sensors required compared to conventional sensor-based methods. Therefore, the cost and complexity of the system are reduced, which in turn improves the converter’s overall reliability. The proposed method employs an exponentially weighted recursive least square (ERLS) algorithm to estimate the SM capacitor voltages through the measured total arm voltage and the switching patterns of each SM. There is thus no need for extra sensors to measure these control signals as they are directly provided from the controller. The robustness of the proposed method is confirmed via introducing deviations for the capacitance values, dynamic load changes, DC voltage change and start-up transient condition. Simulation and experimentally validated results based on a single-phase MMC show the effectiveness of the proposed method in both, steady-state and dynamic operations.

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Divisions: Engineering & Applied Sciences
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: Libyan Ministry of Higher Education and Scientific Research and Scholar Program of Zawia and Sabratha Universities
Uncontrolled Keywords: Modular multilevel converter (MMC),reduced number of sensors, pulse width modulation (PWM),; recursive least square (RLS), voltage balancing control algorithm
Full Text Link: http://ieeexplo ... cument/8268086/
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PURE Output Type: Article
Published Date: 2018-01-24
Published Online Date: 2018-01-24
Accepted Date: 2018-01-04
Authors: Abushafa, Osama Sh. Mohamed
Gadoue, Shady
S. A. Dahidah, Mohamed
Atkinson, David
Missailidis, Petros



Version: Accepted Version

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