Pan, X, Zhang, L and Li, Y (2021) Modulated Model Predictive Control with Common Voltage Injection for MMCC-STATCOM Under Unbalanced Load. In: 2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG). 2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 28 Jun - 01 Jul 2021, Online. IEEE , pp. 1-7. ISBN 978-1-6654-4643-3
Abstract
This paper presents a novel modulated model predictive control (MMPC) scheme for Modular Multilevel Cascaded Converter-based STATCONs (MMCC-STATCOM) to regulate reactive power flow and compensate unbalanced load current. The method imposes a common mode voltage (CMV) on the three phase-voltages used in the model for predicting the converter phase current. This results in the phase-voltages implemented naturally containing a zero-sequence element with some low-order harmonics. These are effective in rebalancing the phase active power imbalances caused due to MMCC STATCOM compensating unbalanced load currents, hence eliminating drift in the phase cluster voltage. Moreover the harmonics in the imposed CMV reduces the peak converter phase voltage, so extending the range of compensation. Furthermore a modified branch and bound (B&B) algorithm is developed to evaluate the optimal per-phase switch duty ratios for the cost function minimization. The algorithm is computationally efficient compared to Model Predictive Control schemes using switching state selection approach. Simulation results of this method are presented in the paper and compared favorably with the conventional scheme which relies on evaluating and injecting zero sequence voltage at each sample interval.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Modulated model predictive control (MMPC) , Branch and bound method (B&B) , Multilevel modular cascaded converter-based STATCOM (MMCC-STATCOM) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Mar 2022 15:45 |
Last Modified: | 08 Mar 2022 15:45 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/pedg51384.2021.9494216 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184481 |