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Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

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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)

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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.