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

Elias Kaufhold,Simon Grandl,Peter Schegner,Jan Meyer

doi:10.3390/en14082118

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.

Highlights

The aim of this study is to assess and demonstrate the feasibility of artificial neural networks (ANN) to reflect the frequency couplings by a time domain model and to handle dynamic events, i.e., to perform the transition between two steady states
Two main aspects were the focus of this study: the feasibility of ANNs to reflect the accurate steady steady state state behavior, behavior, i.e., the frequency couplings, in a time domain model and accurate second, the the feasibility feasibility of of ANNs
Two main aspects were the focus of this study: the feasibility of ANNs to reflect the accurate steady state behavior, i.e., the frequency couplings, in a time domain model and second, the feasibility of ANNs to predict the transition from one steady state into another

Summary

Introduction

Academic Editor: Frede BlaabjergReceived: 10 March 2021Accepted: 7 April 2021Published: 10 April 2021Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland.Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).To pursue the climate goals, a trend from central power generation with a small number of large power generators to a large number of small power generators, such as wind turbines and photovoltaics (PV) can be observed [1]. In medium voltage (MV)networks and high voltage (HV) networks, large wind parks and PV power plants are connected to the network at one Point of Common Coupling (PCC). In low voltage (LV)

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Conclusion