Fluctuations in the Output Power of Photovoltaic Systems Distributed Across a Town with an Isolated Power System Using High-Resolution Data

Moghbel, Moayed; Glenister, Simon; Calais, Martina; Shahnia, Farhad; Carter, Craig; Edwards, David L.; Stephens, David A.; Jones, L.; Trinkl, Pierce

doi:10.1109/icpes47639.2019.9105539

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…With respect to PV, shut downs of inverters have similarly been reported in the past, e.g., [6,7]. Further observed effects of the interaction of PV inverters and distribution systems are unwanted tripping [8] and the impact of resonances, e.g., [9]. Since in future the electrical power generation is expected to be dominated by renewables, these interactions will not only be important for the grid-connected devices but also for the stability of the network itself.…”

Section: Introductionmentioning

confidence: 63%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 63%

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

et al. 2021

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“…More recently, this phenomenon were also measured in an LV network in Switzerland, where a sudden change in the network impedance characteristic due to the operation of a series voltage regulator was identified as reason for the instability of a PV inverter [15]. Especially for the studies of [13][14][15], the reproduction of the scenario to replicate the instabilities has faced major challenges, since the black-box models of the individual devices were missing and no general method to analyze commercially available devices on the basis of measurements is available.…”

Section: Introductionmentioning

confidence: 91%

“…The first known harmonic instability occurred in the Swiss railway grid [12], and further occurrences have been reported, e.g., in the Netherlands [13] and Australia [14]. More recently, this phenomenon were also measured in an LV network in Switzerland, where a sudden change in the network impedance characteristic due to the operation of a series voltage regulator was identified as reason for the instability of a PV inverter [15].…”

Section: Introductionmentioning

confidence: 95%

Measurement-Based Black-Box Harmonic Stability Analysis of Commercial Single-Phase Inverters in Public Low-Voltage Networks

Kaufhold

Meyer

Schegner

2022

Solar

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This paper presents a measurement-based stability analysis of commercially available single-phase inverters in public low-voltage networks. In practice, manufacturers typically do not disclose the parameters of the inverter design, although interactions with the low-voltage network need to be assessed and predicted. State-of-the-art modeling methods require knowledge about the internal parameters. The method proposed in the paper is based on measurements in the laboratory and does not require detailed knowledge about the specific inverter design for the identification of the black-box linear time-periodic representation. The gained information is used for the black-box stability analysis in the frequency range up to 2 kHz, which covers the bandwidth of the control of the inverters. The method is validated for a commercially available photovoltaic inverter in the laboratory. An instability that leads to a shutdown of the inverter is demonstrated, while the critical frequency range is predicted accurately.

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“…This paper presents the analysis of the PV output power fluctuations in an isolated power network and the learning's, which is an expansion of the work presented in [23]. The impact of cloud movement and inverter tripping on the PV output power fluctuations are discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Output power fluctuations of distributed photovoltaic systems across an isolated power system: insights from high‐resolution data

Moghbel

Glenister

Shoeb

et al. 2020

IET Renewable Power Generation

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Deployment of distributed energy resources has rapidly increased during the last few years. The uptake of renewable energy and especially photovoltaic (PV) systems are of interest to utilities in remote and rural areas where the use of conventional power generation is costly. Investigating the effects of such PV systems on isolated power systems at different penetration levels is a relevant research topic. This study reports on the data acquisition system deployed in a remote town in Western Australia and presents some of the findings and observations extracted from the captured real data. It highlights the maximum PV output variations and investigates the underlying factors. The impact of the PV systems on the voltage across the network is also analysed in this study. The studies show that inverter tripping events have led to larger PV output variations in shorter intervals while the cloud movements have contributed to variations in longer intervals.

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Fluctuations in the Output Power of Photovoltaic Systems Distributed Across a Town with an Isolated Power System Using High-Resolution Data

Cited by 5 publications

References 19 publications

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

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

Measurement-Based Black-Box Harmonic Stability Analysis of Commercial Single-Phase Inverters in Public Low-Voltage Networks

Output power fluctuations of distributed photovoltaic systems across an isolated power system: insights from high‐resolution data

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