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@PHDTHESIS{Mirz:804608,
author = {Mirz, Markus},
othercontributors = {Monti, Antonello and Benigni, Andrea},
title = {{A} dynamic phasor real-time simulation based digital twin
for power systems; 1. {A}uflage},
volume = {82},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {E.ON Energy Research Center, RWTH Aachen University},
reportid = {RWTH-2020-10412},
isbn = {978-3-942789-81-3},
series = {E.ON Energy Research Center : ACS, Automation of complex
power systems},
pages = {1 Online-Ressource (xiii, 130 Seiten) : Illustrationen,
Diagramme},
year = {2020},
note = {Auch veröffentlicht auf dem Publikationsserver der RWTH
Aachen University; Dissertation, RWTH Aachen University,
2020},
abstract = {Electrical power systems are becoming interdisciplinary
with power electronics and more digital control algorithms
entering the field. This development demands for
comprehensive testing of new equipment and algorithms before
deployment since power systems must be highly reliable.
Pilot projects provide valuable insights but they do not
offer the flexibility and reproducibility of simulation
based testing environments. Hence, the concept of digital
twins, an equivalent software simulation of physical assets,
is becoming increasingly relevant in product development.
Executing the simulation in real-time further broadens the
spectrum of development stages that can be supported by
simulation because the digital twin can be interfaced with
hardware prototypes in Hardware-In-the-Loop experiments and
with automation systems. However, the increase of power
electronics introducing wide bandwidth signals and larger
system sizes related to the interconnection of national
power systems complicate the simulation of modern power
systems in real-time. The maximum system size can be
increased by running a distributed real-time simulation,
which is challenging due to the small time steps required
for ElectromagneticTransient (EMT) simulations typically
used when considering network dynamics. An alternative is to
simplify the simulation model and consider different time
constants in order to reduce the required computation
resources. Current simulation solutions though are highly
specialized to one or few of the time constants present in
power systems and the associated modelling domain, for
example EMT or quasi-stationary phasors. Transferring
simulation models is difficult due to the variety of
modelling domains, computing technologies and input data
formats. This thesis applies the dynamic phasor approach to
real-time power system simulation to remove the requirement
of proportionality betweenthe simulation time step and the
highest frequency considered in the simulated signals.
Especially for power electronics and
geographicallydistributed real-time simulation, this is an
interesting feature. However, the real-time execution and
large scale simulation are rendered moredifficult by the
increase of variables when using multiple dynamic phasors to
represent a single physical signal. To address this
challenge, a new power system simulator is developed in the
scope of the thesis, which integrates traditional power
system components and power electronics, two domains that
are usually treated separately in dynamic phasor related
literature. The simulator decomposes the system model into
subsystems, each featuring a subset of the network nodes and
the considered frequency bands. Consequently, it executes a
data dependency analysis to determine a schedule for solving
these subsystems and take advantage of parallelization. The
scalability of the simulator is presented for models
featuring a large number of electrical nodes and a wide
frequency spectrum related to detailed power electronics
models. Further examples demonstrate the advantage of
dynamic phasors with respect to EMT simulations in terms of
accuracy for larger simulation time steps. Eventually, the
developed solution offers the user the flexibility to
optimize for smaller simulation time steps and detailed
results or large system size without having to replace
models and input data of the simulation.},
cin = {616310 / 080052},
ddc = {621.3},
cid = {$I:(DE-82)616310_20140620$ / $I:(DE-82)080052_20160101$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2020-10412},
url = {https://publications.rwth-aachen.de/record/804608},
}
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