Data-Driven Emergency Frequency Control for Multi-Infeed Hybrid AC-DC System

Cao, Qianni; Shen, Chen; Liu, Ye

doi:10.1109/tpwrs.2023.3280074

Cited by 3 publications

(1 citation statement)

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“…In Ref. [16], the impact of representation errors of Koopman eigenpairs on the control performance of LQR controllers was evaluated. The adaptive nature of closed-loop control theoretically allows one to compensate for modeling discrepancies and to account for disturbances [14].…”

Section: B Literature Reviewmentioning

confidence: 99%

A Data-Driven Under Frequency Load Shedding Scheme in Power Systems

Golpîra

Bevrani

Messina

et al. 2023

IEEE Trans. Power Syst.

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Under frequency load shedding (UFLS) constitutes the very last resort for preventing total blackouts and cascading events. Fluctuating operating conditions and weak resilience of the future grid require UFLS strategies adapt to various operating conditions and non-envisioned faults. This paper develops a novel data-enabled predictive control algorithm KLS to achieve the optimal one-shot load shedding for power system frequency safety. Our approach yields a network that facilitates a coordinate transformation from the delay embedded space to a new space, wherein the dynamics can be expressed in a linear manner. The network is specifically tailored to effectively track parameter variations in the dynamic model of the system. To address approximation inaccuracies and the discrete nature of load shedding, a safety margin tuning scheme is integrated into the KLS framework, ensuring that the system frequency trajectory remains within the safety range. Simulation results show the adaptability, prediction capability and control effect of the proposed UFLS strategy.

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Section: B Literature Reviewmentioning

confidence: 99%