TY - JOUR
T1 - An Adaptive Inertial Control Strategy for Wind Turbines via Fuzzy Logic and OPPTE Integration
AU - Loza, Brian
AU - Minchala, Luis I.
AU - Ochoa-Correa, Danny
AU - Arévalo-Cordero, Paul
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/12
Y1 - 2025/12
N2 - The increasing integration of wind power into modern power systems has fostered the demand for reliable frequency regulation strategies, with inertial control emerging as a key solution that utilizes the kinetic energy stored in the wind turbine rotors. Traditional inertial controllers, however, usually depend on fixed gain parameters, which restrict their adaptability under changing grid conditions. This paper introduces a new inertial control strategy that combines a fuzzy logic controller with the Extended Optimized Power Point Tracking (OPPTE) algorithm to improve the frequency response of wind turbines. The fuzzy logic system allows adaptive control by responding dynamically to both frequency deviations and their rate of change, thereby adjusting the turbine’s operating point during emergency events. By shifting the operating point, the system can release more kinetic energy at critical moments, resulting in improved active power injection. The proposed approach was tested through simulation studies in MATLAB/Simulink R2024b using a detailed wind turbine model under various contingency scenarios. The results obtained demonstrate that the proposed strategy surpasses the conventional OPPTE method by significantly improving the maximum value of active power injected into the electrical grid by 6.56% and 9.68% under constant wind and wind series conditions, respectively, as well as reductions in the frequency nadir of 9.6% and 6.4%, and decreases in the frequency change rate of 5% and 4.57% in the exact scenarios. These results demonstrate that combining fuzzy logic with dynamic operating point adjustment provides a practical and effective way to strengthen inertial support and improve grid stability in power systems with high wind power integration.
AB - The increasing integration of wind power into modern power systems has fostered the demand for reliable frequency regulation strategies, with inertial control emerging as a key solution that utilizes the kinetic energy stored in the wind turbine rotors. Traditional inertial controllers, however, usually depend on fixed gain parameters, which restrict their adaptability under changing grid conditions. This paper introduces a new inertial control strategy that combines a fuzzy logic controller with the Extended Optimized Power Point Tracking (OPPTE) algorithm to improve the frequency response of wind turbines. The fuzzy logic system allows adaptive control by responding dynamically to both frequency deviations and their rate of change, thereby adjusting the turbine’s operating point during emergency events. By shifting the operating point, the system can release more kinetic energy at critical moments, resulting in improved active power injection. The proposed approach was tested through simulation studies in MATLAB/Simulink R2024b using a detailed wind turbine model under various contingency scenarios. The results obtained demonstrate that the proposed strategy surpasses the conventional OPPTE method by significantly improving the maximum value of active power injected into the electrical grid by 6.56% and 9.68% under constant wind and wind series conditions, respectively, as well as reductions in the frequency nadir of 9.6% and 6.4%, and decreases in the frequency change rate of 5% and 4.57% in the exact scenarios. These results demonstrate that combining fuzzy logic with dynamic operating point adjustment provides a practical and effective way to strengthen inertial support and improve grid stability in power systems with high wind power integration.
KW - fast-frequency response
KW - frequency control
KW - fuzzy logic control
KW - inertial control
KW - operating point
KW - wind power integration
UR - https://www.scopus.com/pages/publications/105025907948
U2 - 10.3390/technologies13120547
DO - 10.3390/technologies13120547
M3 - Artículo
AN - SCOPUS:105025907948
SN - 2227-7080
VL - 13
JO - Technologies
JF - Technologies
IS - 12
M1 - 547
ER -