Data-Driven Dynamic Model Identification for Synchronous Generators

Wang, Zhengyu; Fan, Lingling; Miao, Zhixin

doi:10.1109/naps46351.2019.9000184

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Besides, some parameter information can be inferred from interrelated data. Specifically, with periodically collected measurements,

S_{ki}

and

\rho _j

could be estimated from the power flow model and the generation dispatch model, respectively [55, 56]. The same goes for the DR model and load forecast model [57, 58].…”

Section: Real‐time Pricing Response Attackmentioning

confidence: 99%

Real‐time pricing response attack in smart grid

Liu

Tian

Yuan

et al. 2022

IET Generation Trans & Dist

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Real-time pricing is an attractive tariff for demand response programs, in which consumers could provide load flexibility by adjusting their electricity consumption in response to timevarying electricity prices. This paper reveals a new threat called real-time pricing response attack (RPRA), which manipulates consumers' electricity consumption by tampering with electricity price signals. Simulations are performed in IEEE 24-bus, 39-bus, and 118-bus systems, and the results show that 8% to 28% of the transmission lines could be overloaded by the attack, which could cause serious security problems. Results of sensitivity analysis indicate that the attack effects are influenced by various factors, such as DR models, price elasticity, line capacities etc. To defend against the RPRA attack, we have proposed various detection and mitigation strategies.

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“…Besides, some parameter information can be inferred from interrelated data. Specifically, with periodically collected measurements,

S_{ki}

and

\rho _j

could be estimated from the power flow model and the generation dispatch model, respectively [55, 56]. The same goes for the DR model and load forecast model [57, 58].…”

Section: Real‐time Pricing Response Attackmentioning

confidence: 99%

Real‐time pricing response attack in smart grid

Liu

Tian

Yuan

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

A new method to identify synchronous generator and turbine‐governor parameters of a gas unit using a closed‐loop model

Zaker

Khalili

Rabieyan

et al. 2021

Int Trans Electr Energ Syst

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Summary In this paper, an efficient three‐stage algorithm for identifying the turbine‐governor (TG) and synchronous generator (SG) parameters is proposed. Instead of considering approximate models, precise models of the TG and SG are used. In the proposed approach, d‐q axes parameters of SG, mechanical parameters of SG, and TG model parameters are estimated. For the TG model, all the main control loops are carefully modeled with respect to the real‐world system. Moreover, the robustness of the governor is enhanced by employing an anti‐windup configuration for the load‐speed controller. The well‐known seventh‐order SG model is also used. The accuracy of the model and effectiveness of the parameter identification procedure is verified by experimental data. The results show satisfactory numbers for the selected performance index in various operating points when different controllers are in effect.

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Data-Driven Dynamic Model Identification for Synchronous Generators

Cited by 2 publications

References 9 publications

Real‐time pricing response attack in smart grid

Real‐time pricing response attack in smart grid

A new method to identify synchronous generator and turbine‐governor parameters of a gas unit using a closed‐loop model

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