Grid-interactive inverter modeling for power system studies

Ninad, Nayeem; Turcotte, Dave; EL-Fouly, Tarek H. M.

doi:10.1109/pesgm.2015.7286302

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…Detailed models of these converters are often used to perform accurate electromagnetic transient simulations. Though accurate, the complexity of these models are prohibitive to be used for long-term and/or large-scale system studies [28]. Furthermore, the parameters to accurately represent the exact dynamics are difficult to obtain and these methods cannot be employed without knowledge of the topology and control architecture used.…”

Section: Related Work On Data-driven Modeling Approaches For Power El...mentioning

confidence: 99%

Data-Driven Modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation

Guruwacharya,

Chakraborty,

Saraswat

et al. 2024

IEEE Access

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Recently, there is rapid integration of power electronic converter (PECs) into the power grid. Most of these PECs are grid-following inverters, where weak grid operation becomes an issue. Research is now shifting focus to grid-forming (GFM) inverters, resembling synchronous generators. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. Data-driven modeling (DDM) techniques are becoming valuable tools for capturing the dynamic behavior of advanced control strategies for PECs. This paper proposes using power hardware-in-the-loop experiments to capture dynamic GFM data in the application of DDM techniques. Furthermore, the paper derives an analytical approach to obtaining a mathematical model of GFM inverter dynamics and compares it with the DDM. A square-chirp probing signal was employed to perturb the active and reactive power of the load inside an Opal-RT model. The dynamic response of the GFM inverter, including changes in frequency and voltage, was recorded. This data was then used in a system identification algorithm to derive the GFM DDMs. The effectiveness of DDM is cross-validated with an analytical approach through experimental simulation studies, and the goodness-of-fit for both approaches is compared. Both approaches show more than 85% accuracy in capturing the dynamic response of GFM inverters under different loading conditions.INDEX TERMS Data-driven modeling, grid-forming inverter, power hardware-in-the-loop, power system dynamics, real-time digital simulator, system identification.

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Section: Related Work On Data-driven Modeling Approaches For Power El...mentioning

confidence: 99%

Data-Driven Modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation

Guruwacharya,

Chakraborty,

Saraswat

et al. 2024

IEEE Access

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“…Detailed models of these converters are often used to perform accurate electromagnetic transient (EMT) simulations. Though accurate, the complexity of these models are prohibitive to be used for long-term and/or large-scale system studies [9]. Furthermore, the parameters to accurately represent the exact dynamics are difficult to obtain and these methods cannot be employed without knowledge of the topology and control architecture used.…”

Section: Data-driven Modeling Approaches For Power Electronic Convertersmentioning

confidence: 99%

“…In cases where the switching dynamics are neglected, averaging techniques (specifically state-space modeling techniques) are often employed to derive small-signal transfer function models [10]. However, depending on the analysis required even such statespace models may be computationally prohibitive [9].…”

Section: Data-driven Modeling Approaches For Power Electronic Convertersmentioning

confidence: 99%

“…Detailed models can be developed using techniques such as average state-space modeling [11], [13]. These models are very accurate and useful for component and converter level design [6], [9], but they require detailed information about the converters [13], which are often proprietary for commercial converters [12]. Even if some of the internal parameters are known, the converter properties and dynamics may have a wide range of variation depending on load requirements, battery state-of-charge, and renewable energy availability.…”

Section: Data-driven Modeling Approaches For Power Electronic Convertersmentioning

confidence: 99%

“…Even if some of the internal parameters are known, the converter properties and dynamics may have a wide range of variation depending on load requirements, battery state-of-charge, and renewable energy availability. For these reasons, developing simplified models can be beneficial [9].…”

Section: Data-driven Modeling Approaches For Power Electronic Convertersmentioning

confidence: 99%

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Data-Driven Power Electronic Converter Modeling for Low Inertia Power System Dynamic Studies

Guruwacharya

Bhujel

Tamrakar

et al. 2020

2020 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a realtime digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converterdominated electric grids.

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