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|Title:||Model-predictive current control of modular multilevel converters with phase-shifted pulsewidth modulation||Authors:||Zhou, Dehong
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Zhou, D., Yang, S. & Tang, Y. (2019). Model-predictive current control of modular multilevel converters with phase-shifted pulsewidth modulation. IEEE Transactions On Industrial Electronics, 66(6), 4368-4378. https://dx.doi.org/10.1109/TIE.2018.2863181||Journal:||IEEE Transactions on Industrial Electronics||Abstract:||Model-predictive current control (MPCC) is a promising candidate for modular multilevel converter (MMC) control due to its advantages of direct modeling and fast dynamic response. The conventional MPCC, which obtains the optimal control input by evaluating a cost function for all the possible switching states, may make the MPCC impractical due to the exponentially increasing computation burden with the increasing number of submodules (SMs). On the other hand, the MPCC experiences high load current and circulating current tracking errors, since only one switching state is selected and applied during one control period. To address these issues, this paper proposes an MPCC with phase-shifted pulsewidth modulation (PS-PWM) for improving the steady-state control performance. The arm voltages are considered as a whole to implement the proposed MPCC. The optimal duty cycle is obtained based on the load and circulating current tracking error minimization and applied using the PS-PWM. As a result, the computation burden is unrelated to the number of SMs by avoiding the exhaustive evaluation process for all the possible switching states. A better steady-state performance with smaller tracking errors is achieved with the similar switching frequency, and the tedious tuning process of the weighting factor is eliminated. Experimental results are presented to demonstrate the effectiveness of the proposed MPCC.||URI:||https://hdl.handle.net/10356/151570||ISSN:||0278-0046||DOI:||10.1109/TIE.2018.2863181||Rights:||© 2018 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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