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This paper studies the impact factors on the dynamic behavior of single-phase power electronic devices in low-voltage grids. White-box models are able to reflect the dynamic behavior, but require a detailed knowledge of the device. Black-box models can be easily parametrized by measurements, but are limited to small signal studies in frequency domain. No studies exist on black-box approaches reflecting the dynamic behavior of power electronic devices. A systematic, measurement based identification method considering the dynamic behavior is required to develop such dynamic black-box models. The aim of this paper is, to provide an overview of the impact factors on the dynamic behavior of power electronic devices as basis for the development of respective measurement procedures. An identification approach and its possible implementation for a laboratory test stand is proposed. Quantifying the impact factors with respect to the dynamic system response in terms of linear and non-linear characteristic provides the opportunity to develop a set of new dynamic models that can be used to improve e.g. stability studies for low voltage networks with a large penetration of modern power electronic devices in the future. First results are presented exemplarily for photovoltaic inverters.

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This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.