Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks

Elias Kaufhold, Simon Grandl, Jan Meyer and Peter Schegner
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Elias Kaufhold: Institute of Electrical Power Systems and High Voltage Engineering, Technische Universitaet Dresden, 01062 Dresden, Germany
Simon Grandl: Institute of Electrical Power Systems and High Voltage Engineering, Technische Universitaet Dresden, 01062 Dresden, Germany
Jan Meyer: Institute of Electrical Power Systems and High Voltage Engineering, Technische Universitaet Dresden, 01062 Dresden, Germany
Peter Schegner: Institute of Electrical Power Systems and High Voltage Engineering, Technische Universitaet Dresden, 01062 Dresden, Germany

Energies, 2021, vol. 14, issue 8, 1-16

Abstract: 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.

Keywords: artificial intelligence; converter; modeling; photovoltaics; power electronics; power quality (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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