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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

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Development of measurement-based load models for the dynamic simulation of distribution grids

Kontis, Eleftherios O. and Syed, Mazheruddin H. and Guillo-Sansano, Efren and Papadopoulos, Theofilos A. and Chrysochos, Andreas I. and Papagiannis, Grigoris K. and Burt, Graeme M. (2017) Development of measurement-based load models for the dynamic simulation of distribution grids. In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017-09-26 - 2017-09-29. (In Press)

[img] Text (Kontis-etal-IEEE-PES-2017-Development-of-measurement-based-load-models-for-the-dynamic)
Kontis_etal_IEEE_PES_2017_Development_of_measurement_based_load_models_for_the_dynamic.pdf - Accepted Author Manuscript
Restricted to Repository staff only until 26 September 2017.

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Abstract

The advent of new types of loads, such as power electronics and the increased penetration of low-inertia motors in the existing distribution grids alter the dynamic behavior of conventional power systems. Therefore, more accurate dynamic, aggregate, load models are required for the rigorous assessment of the stability limits of modern distribution networks. In this paper, a measurement-based, input/output, aggregate load model is proposed, suitable for dynamic simulations of distribution grids. The new model can simulate complex load dynamics by employing variable-order transfer functions. The minimum required model order is automatically determined through an iterative procedure. The applicability and accuracy of the proposed model are thoroughly evaluated under distinct loading conditions and network topologies using measurements acquired from a laboratory-scale test setup. Furthermore, the performance of the proposed model is compared against other conventional load models, using the mean absolute percentage error.