Derivations of Point-2 and Point-3 Time-Sequence Extrapolation Formulas

Zhang, Yunong; Li, Litian

doi:10.23919/ccc55666.2022.9902283

Cited by 3 publications

(3 citation statements)

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“…v i =v iα +jv iβ ; v c =v cα +jv cβ ; v g =v gα +jv gβ (46) where the relationship between the per-phase and alpha-beta quantities is given in (47).…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…x a x b x c (47) Depending on the inverter topology, some algorithms also use filter capacitor voltage as the controlled state [46]. Now, the predicted controlled state, i g,k+1 , is compared against the reference value, i * g , in the objective function (48), which is evaluated for each possible switching.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…Then, using four-term Langrage's extrapolation, i * gα,k+1 is computed as in (50) (see Table 1 of [47]).…”

Section: Model Predictive Controlmentioning

confidence: 99%

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Reviewing Control Paradigms and Emerging Trends of Grid-Forming Inverters—A Comparative Study

Rahman,

Hashimoto,

Orihara

et al. 2024

Energies

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Grid-forming inverters (GFMs) have emerged as crucial components in modern power systems, facilitating the integration of renewable energy sources and enhancing grid stability. The significance of GFMs lies in their ability to autonomously establish grid voltage and frequency, enabling grids to form and improve system flexibility. Discussing control methods for grid-forming inverters is paramount due to their crucial role in shaping grid dynamics and ensuring reliable power delivery. This paper explores the fundamental and advanced control methods employed by GFMs, explaining their operational principles and performance characteristics. Basic control methods typically involve droop control, voltage and frequency regulation, and power-balancing techniques to maintain grid stability under varying operating conditions. Advanced control strategies encompass predictive control, model predictive control (MPC), and adaptive control, which influence advanced algorithms and real-time data for enhanced system responsiveness and efficiency. A detailed analysis and performance comparison of different control methods for GFM is presented, highlighting their strengths, limitations, and suitability for diverse grid environments. Through comprehensive studies, this research interprets the ability of various control strategies to mitigate grid disturbances, optimize power flow, and enhance overall system stability.

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“…v i =v iα +jv iβ ; v c =v cα +jv cβ ; v g =v gα +jv gβ (46) where the relationship between the per-phase and alpha-beta quantities is given in (47).…”

Section: Model Predictive Controlmentioning

confidence: 99%