2018
DOI: 10.1109/tpel.2017.2727496
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Energy-Based State-Space Representation of Modular Multilevel Converters with a Constant Equilibrium Point in Steady-State Operation

Abstract: The internal currents and voltages of Modular Multilevel Converters (MMCs) contain multiple frequency components in steady state operation and remain time-periodic even when transformed into a synchronously rotating reference frame. This prevents a straightforward state-space representation where a constant equilibrium point is reached and all state variables converge to constant values under steady-state conditions. Such steady-state time-invariant (SSTI) representations are needed for linearization and eigen… Show more

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Cited by 56 publications
(54 citation statements)
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“…The modular multilevel converter is the most promising converter technology for MT-HVDC system in offshore applications. For the purpose of power system studies, its dynamics is described by the continuous model presented in [15]- [17]. Initially, the n submodule dynamic equivalents depicted in Fig.…”
Section: A Model Of the Modular Multilevel Convertermentioning
confidence: 99%
“…The modular multilevel converter is the most promising converter technology for MT-HVDC system in offshore applications. For the purpose of power system studies, its dynamics is described by the continuous model presented in [15]- [17]. Initially, the n submodule dynamic equivalents depicted in Fig.…”
Section: A Model Of the Modular Multilevel Convertermentioning
confidence: 99%
“…Eq. (2) gives a Steady State Time Periodic (SSTP) model of the MMC, whereas a Steady State Time Invariant (SSTI) model is needed for small signal studies [6]. Dynamic phasor based modelling [8] of the harmonic components is used in order to obtain an SSTI model.…”
Section: Detailed Average Modellingmentioning
confidence: 99%
“…The second harmonic part is given by (6). (4) to (6) can be extended to the F2F by duplicating all the states except the ac current, because it is common to both sides.…”
Section: Detailed Average Modellingmentioning
confidence: 99%
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