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@PHDTHESIS{Wu:689766,
      author       = {Wu, Xiang},
      othercontributors = {Monti, Antonello and Moser, Albert},
      title        = {{N}ew approaches to dynamic equivalent of active
                      distribution network for transient analysis; 1. {A}uflage},
      volume       = {40},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {E.ON Energy Research Center, RWTH Aachen University},
      reportid     = {RWTH-2017-04572},
      isbn         = {978-3-942789-39-4},
      series       = {E.On Energy Research Center : ACS, Automation of complex
                      power systems},
      pages        = {xviii, 138 Seiten : Illustrationen, Diagramme},
      year         = {2016},
      note         = {Druckausgabe: 2016. - Auch veröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University 2017;
                      Dissertation, RWTH Aachen University, 2016},
      abstract     = {With the increasing amount of distributed generators at the
                      distribution level, the dynamic behavior of active
                      distribution networks (ADNs) will have a more significant
                      influence on the overall electrical system. To perform
                      transient analysis of such a large and complex system, it is
                      neither practical nor necessary to apply a fully detailed
                      system model. A technique for obtaining highly accurate yet
                      simple equivalents for ADNs is becoming increasingly
                      important. In this dissertation, three original equivalent
                      models are proposed to cover the challenges posed by the
                      size and complexity of power system transient analysis. A
                      fixed-structure dynamic equivalent model (FDEM) is proposed
                      by integrating four individual equivalent models (IEMs),
                      which are derived from approximation of the physical models
                      of electrical equipment. The FDEM appears as a sixth- order
                      state space, which is much less complex than the original
                      systems. It can be easily integrated into different tools as
                      a modular component with to-be-edited parameters. The
                      derivation of the IEMs and FDEM presents the first original
                      contribution. An adaptive dynamic equivalent model (ADEM) is
                      proposed by formulating an equivalence problem in terms of a
                      Markov decision process problem, which is solved using a
                      machine learning algorithm based on reinforcement learning.
                      The structure of the ADEM is adaptive depending on measured
                      data and it can be directly applied for on-line
                      applications. It keeps not only a simple equivalent model
                      form but also brings flexibility for an equivalent model
                      structure. The transformation of the equivalence problem to
                      Markov decision process problem and the learning skills of
                      the ADEM are the second original contribution. A random
                      forest-based dynamic equivalent model (RF-DEM) is proposed
                      by introducing randomized learning framework with feedbacks
                      from outputs, which trains the relationship between inputs
                      and outputs using RF as the supervised learning algorithm.
                      The RF-DEM takes advantage of easy implementation and does
                      not require electrical modeling and approximation knowledge
                      for deriving the equivalent models. The design of the RF-DEM
                      forms the third original contribution.},
      cin          = {616310 / 080052},
      ddc          = {621.3},
      cid          = {$I:(DE-82)616310_20140620$ / $I:(DE-82)080052_20160101$},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2017-04572},
      url          = {https://publications.rwth-aachen.de/record/689766},
}