Identification based Dynamic Equivalencing

Feng, X.; Lubośny, Zbigniew; Białek, Janusz

doi:10.1109/pct.2007.4538328

Cited by 28 publications

(24 citation statements)

References 1 publication

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“…The first group requires no knowledge about the configuration and parameters of the external system. Usually these methods solely utilize measurements from the study system and its boundary (for example 1 those described in [2,3]). The second group requires knowledge about the external system and the methods are generally referred to as model reduction methods.…”

Section: Introductionmentioning

confidence: 99%

Equivalent to represent inertial and primary frequency control effects of an external system

Weckesser¹,

Cutsem

2017

IET Generation, Transmission & Distribution

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This paper presents a novel equivalent, which is suitable for simulation of inertial and primary frequency control effects. In the model reduction procedure, dynamic power injectors are used to replace the external system and to mimic its dynamic behavior. The parameters of the equivalents are tuned with a simple approach presented in this paper. The effectiveness of the proposed method is demonstrated on a modified version of the ENTSO-E Dynamic Study Model. The results show that the system frequency response of the unreduced system is retained and a speedup of the simulations of around 4.0 is achieved.

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Section: Introductionmentioning

confidence: 99%

Equivalent to represent inertial and primary frequency control effects of an external system

Weckesser¹,

Cutsem

2017

IET Generation, Transmission & Distribution

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“…For the analysis of the MGs dynamics, black-or grey-box modelling techniques are used in [23]- [27] to develop equivalent This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution, and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library, or http://dx.doi.org/10.1049/ietgtd.…”

Section: Introductionmentioning

confidence: 99%

“…The model parameters are directly extracted from measurements without prior knowledge of the exact MG features and structure. However, system identification methods are applied in MGs only on simulation data [23]- [27]. Therefore, the need to ensure the validity of the results from application of system identification methods on real measurement data from MGs is crucial.…”

Section: Introductionmentioning

confidence: 99%

Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

Papadopoulos

Crolla³

et al. 2015

IET Generation, Transmission & Distribution

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The dynamic performance of microgrids is of crucial importance, due to the increased complexity introduced by the combined effect of inverter interfaced and rotating distributed generation. This paper presents a methodology for the investigation of the dynamic behavior of microgrids based on measurements using Prony analysis and state-space black-box modeling techniques. Both methods are compared and evaluated using real operating conditions data obtained by a laboratory microgrid system. The recorded responses and the calculated system eigenvalues are used to analyze the system dynamics and interactions among the distributed generation units. The proposed methodology can be applied to any real-world microgrid configuration, taking advantage of the future smart grid technologies and features.

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“…Furthermore, modelling restrictions due to the network complexity, generation mix and control strategies are overridden by the black-box approach, ensuring the flexibility and generalized form of the developed models. The black-box parameters are derived using system identification techniques such as sub-space methods [17], [18] and Prony analysis [19], [20]. In most cases simulation results are used for the model parameter identification and validation, whereas in some cases field measurements from conventional, extended transmission networks [19].…”

Section: Introductionmentioning

confidence: 99%

“…Measurement data are used to extract the model parameters, which can be updated online, providing an accurate representation of the changing system structure. The model can be combined with power system analysis software packages and can be implemented as a portable network feeder element, representing the MG system [14], [18]. The proposed model is used in several MG topologies including both rotating and inverter interfaced DG units with different control strategies.…”

Section: Introductionmentioning

confidence: 99%