TY - JOUR
T1 - Droop control in grid-forming converters using a fractional-order PI controller
T2 - A power system transient analysis
AU - Chiza, Luis L.
AU - Benítez, Diego
AU - Aguilar, Rommel
AU - Camacho, Oscar
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/3
Y1 - 2025/3
N2 - The integration of renewable energy sources in modern power grids introduces challenges in ensuring stable and efficient operation, especially during transient conditions and disturbances. One of the primary issues is the inadequate transient response of conventional droop control strategies in grid-forming (GFM) converters, which can impair system stability and performance under unbalanced load conditions. This article addresses these issues by introducing a fractional-order PI (FOPI) control strategy for droop control of GFM converters, aimed at improving the transient response and enhancing the overall stability of the system. The FOPI controller's design allows for more flexible tuning of dynamic behaviors compared to traditional integer-order controllers, making it particularly effective for bolstering stability and fault tolerance. To optimize the parameters of the FOPI controller, continuous Monte Carlo simulation is used, focusing on performance under unbalanced load disturbances. The controller's effectiveness is assessed using the Integral of Squared Error (ISE) and Integral of Squared Control Output (ISCO) metrics to balance accuracy and control effort. The simulation results under two fault scenarios demonstrate that the FOPI controllers significantly enhance the transient response and fault tolerance. In case 1, replacing the PI controllers with the FOPI controllers reduces error by 65% and improves energy efficiency by 16%. In case 2, FOPI controllers achieve an error reduction of 83% and an improvement in energy efficiency 15%. These findings highlight the effectiveness of FOPI controllers in improving control accuracy and efficiency in fault conditions.
AB - The integration of renewable energy sources in modern power grids introduces challenges in ensuring stable and efficient operation, especially during transient conditions and disturbances. One of the primary issues is the inadequate transient response of conventional droop control strategies in grid-forming (GFM) converters, which can impair system stability and performance under unbalanced load conditions. This article addresses these issues by introducing a fractional-order PI (FOPI) control strategy for droop control of GFM converters, aimed at improving the transient response and enhancing the overall stability of the system. The FOPI controller's design allows for more flexible tuning of dynamic behaviors compared to traditional integer-order controllers, making it particularly effective for bolstering stability and fault tolerance. To optimize the parameters of the FOPI controller, continuous Monte Carlo simulation is used, focusing on performance under unbalanced load disturbances. The controller's effectiveness is assessed using the Integral of Squared Error (ISE) and Integral of Squared Control Output (ISCO) metrics to balance accuracy and control effort. The simulation results under two fault scenarios demonstrate that the FOPI controllers significantly enhance the transient response and fault tolerance. In case 1, replacing the PI controllers with the FOPI controllers reduces error by 65% and improves energy efficiency by 16%. In case 2, FOPI controllers achieve an error reduction of 83% and an improvement in energy efficiency 15%. These findings highlight the effectiveness of FOPI controllers in improving control accuracy and efficiency in fault conditions.
KW - Fractional-order PI
KW - Grid-forming
KW - Monte carlo simulation
KW - Optimal tuning
KW - Power systems
KW - Transient analysis
UR - http://www.scopus.com/inward/record.url?scp=85214301252&partnerID=8YFLogxK
U2 - 10.1016/j.rico.2025.100517
DO - 10.1016/j.rico.2025.100517
M3 - Artículo
AN - SCOPUS:85214301252
SN - 2666-7207
VL - 18
JO - Results in Control and Optimization
JF - Results in Control and Optimization
M1 - 100517
ER -