Direct Power Control (DPC) is widely employed in wind power systems (WPSs) due to its simplicity, effectiveness, and ability to manage the power of WPSs. However, conventional DPC suffers from power quality issues, including harmonic distortion, current ripple, uncertainty, and reduced efficiency when the system parameters vary significantly. To address these issues, this work presents a new, straightforward, and more reliable control approach based on a proportional-integral regulator, known as cascade control (CC). The gains of this CC were optimized via a genetic algorithm (GA). This CC-GA approach is applied to doubly fed induction generators (DFIGs) with rotor-side converter (RSC) management, which are commonly used generators in WPSs. This is accomplished by regulating the RSC pulse-width modulation. Moreover, effective control of the output power of twin-rotor large wind turbines is essential to ensure maximum energy extraction under varying wind conditions. The proposed CC-GA outperforms some DPC techniques in terms of power quality under both normal and abnormal operating conditions. The effectiveness of the presented CC-GA approach is confirmed in terms of robustness, dynamic rapid response, current fitness, and ripple ratio.
PalleswariYTRVanithaR. A novel hybrid high gain DC-DC converter for renewable energy applications. Int J Renew Energy Resour2022; 12(1): 88–96. https://doi.org/10.20508/ijrer.v12i1.12554.g8376
YasserENaggarHSAbdalhalimZ. Assessing wind energy conversion systems based on newly developed wind turbine emulator. International Journal of Smart Grid-ijSmartGridvol2020; 4(4): 139–148. https://doi.org/10.20508/ijsmartgrid.v4i4.133.g101
4.
HamzaSSaidDLarbiC.-A. Second order sliding mode control of DC-DC converter used in the photovoltaic system according an adaptive MPPT. Int J Renew Energy Resour2016; 6(2): 375–383. https://doi.org/10.20508/ijrer.v6i2.3369.g6797
5.
KavyaSKanakasabapathyPSaikatC, et al.Stability assessment in radial distribution systems: leveraging artificial neural networks for high penetration of solar PV and EVs. International Journal of Smart Grid-ijSmartGrid2025; 9(3): 158–175. https://doi.org/10.20508/ijsmartgrid.v9i3.529.g396
6.
LaxmanraoSPCijiPKBirendraKS, et al.Simulation and control of DC/DC converter for MPPT based hybrid PV/Wind power system. International Journal of Renewable Energy Research-IJRER2014; 4(3): 801–809. https://doi.org/10.20508/ijrer.v4i3.1543.g6406
7.
ShahFNasimUAliJM, et al.An advanced two-stage grid connected PV system: a fractional-order controller. International Journal of Renewable Energy Research-IJRER2019; 9(1): 504–514. https://doi.org/10.20508/ijrer.v9i1.9031.g7609
8.
MaiTHandMMBaldwinSF, et al.Renewable electricity futures for the United States. IEEE Trans Sustain Energy2014; 5(2): 372–378. https://doi.org/10.1109/TSTE.2013.2290472
9.
BenbouhenniHBizonN. Improved rotor flux and torque control based on the third-order sliding mode scheme applied to the asynchronous generator for the single-rotor wind turbine. Mathematics2021; 9(18): 2297. https://doi.org/10.3390/math9182297
10.
BenbouhenniHBizonN. Advanced direct vector control method for optimizing the operation of a double-powered induction generator-based dual-rotor wind turbine system. Mathematics2021; 9(19): 2297. https://doi.org/10.3390/math9182297
11.
ElkodamaAIsmaielAAbdellatifA, et al.Aerodynamic performance and structural design of 5 MW multi rotor system (MRS) wind turbines. Int J Renew Energy Resour2022; 12(3): 1495–1505.
12.
ErcanESelimSFevziCB. Analysis model of a small scale counter-rotating dual rotor wind turbine with double rotational generator armature. Int J Renew Energy Resour2018; 8(4): 1849–1858.
13.
HabibBNicuB. Terminal synergetic control for direct active and reactive powers in asynchronous Generator-based dual-rotor wind power systems. Electronics2021; 10(16): 1–23. https://doi.org/10.3390/electronics10161880
14.
BenbouhenniHBizonN. Third-order sliding mode applied to the direct field-oriented control of the asynchronous generator for variable-speed contra-rotating wind turbine generation systems. Energies2021; 14(18): 1–17. https://doi.org/10.3390/en14185877
15.
KadiSImarazeneKEl MadjidB, et al.A direct vector control based on modified SMC theory to control the double-powered induction generator-based variable-speed contra-rotating wind turbine systems. Energy Rep2022; 8: 15057–15066. https://doi.org/10.1016/j.egyr.2022.11.052
16.
BorisDBanePDraganM, et al.Artificial intelligence based vector control of induction generator without speed sensor for use in wind energy conversion system. Int J Renew Energy Resour2015; 5(1): 299–307. https://doi.org/10.20508/ijrer.v5i1.2030.g6498
17.
YuLZhangZChenZ, et al.Analysis and verification of the doubly salient brushless DC generator for automobile auxiliary power unit application. IEEE Trans Ind Electron2014; 61(12): 6655–6663. https://doi.org/10.1109/TIE.2014.2320224
18.
MaiNAAhmedADMedhatHE. A novel model predictive speed controller for PMSG in wind energy systems. Int J Renew Energy Resour2022; 12(1): 170–180. https://doi.org/10.20508/ijrer.v12i1.12750.g8385
19.
GulWGaoQLenwariW. Optimal design of a 5-MW double-stator single-rotor PMSG for offshore direct drive wind turbines. IEEE Trans Ind Appl2020; 56(1): 216–225. https://doi.org/10.1109/TIA.2019.2949545
20.
El-NaggarMFMosaadMIHasanienHM, et al.Elephant herding algorithm-based optimal PI controller for LVRT enhancement of wind energy conversion systems. Ain Shams Eng J2021; 12(Issue 1): 599–608. https://doi.org/10.1016/j.asej.2020.07.013
21.
BizzarriFBrambillaAMilanoF. Simplified model to study the induction generator effect of the subsynchronous resonance phenomenon. IEEE Trans Energy Convers2018; 33(2): 889–892. https://doi.org/10.1109/TEC.2018.2799479
22.
MosaadMI. Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller. Wind Eng2024; 49(1): 129–141. https://doi.org/10.1177/0309524X241256956
23.
RayaneLLekhchineS. Fuzzy logic controller-based power control of DFIG based on wind energy systems. International Journal of Smart Grid-ijSmartGrid2024; 8(1): 74–80. https://doi.org/10.20508/ijsmartgrid.v8i1.334.g346
24.
RezaGAliMAlirezaDS. A new control algorithm method based on DPC to improve power quality of DFIG in unbalance grid voltage conditions. Int J Renew Energy Resour2018; 8(4): 2228–2238. https://doi.org/10.20508/ijrer.v8i4.8583.g7527
25.
HabibB. Rotor flux and electromagnetic torque regulation of DFIG using dual PI controllers. International Journal of Smart Grid-ijSmartGrid2023; 7(4): 227–234. https://doi.org/10.20508/ijsmartgrid.v7i4.308.g311
26.
TavakoliSMPourminaMAZolghadriMR. Comparison between different DPC methods applied to DFIG wind turbines. International Journal of Renewable Energy Research-IJRER2013; 3(2): 446–452. https://doi.org/10.20508/ijrer.v3i2.680.g6162
27.
Sumanth YamparaLALakshminarasimmanLSambasivaRG. Optimal design of FoPID controller for DFIG based wind energy conversion system using grey-wolf optimization algorithm. Int J Renew Energy Resour2022; 12(4): 2111–2120. https://doi.org/10.20508/ijrer.v12i4.13446.g8594
28.
SumanthYSambasivaRGDharaniKN. Optimized FoPID controller for doubly-fed induction generator based wind turbine using root tree optimization algorithm. Int J Renew Energy Resour2025; 15(2): 373–383. https://doi.org/10.20508/ijrer.v15i2.14783.g9061
29.
BekhadaHMamadouLDMohamedB, et al.Comparative study of PI, RST, sliding mode and fuzzy supervisory controllers for DFIG based wind energy conversion system. International Journal of Renewable Energy Research-IJRER2015; 5(4): 1174–1185. https://doi.org/10.20508/ijrer.v5i4.2915.g6705
30.
ChakibMTamouNAhmedE. Contribution of variable speed wind turbine generator based on DFIG using ADRC and RST controllers to frequency regulation. Int J Renew Energy Resour2021; 11(1): 320–331.
31.
MoualdiaAMahmoudiMONezliL, et al.Modeling and control of a wind power conversion system based on the double-fed asynchronous generator. Int J Renew Energy Resour2012; 2(2): 300–306. https://doi.org/10.20508/ijrer.v2i2.193.g118
32.
YoucefBDjilaniBA. Comparison study between SVM and PWM inverter in sliding mode control of active and reactive power control of a DFIG for variable speed wind energy. Int J Renew Energy Resour2012; 2(3): 471–476. https://doi.org/10.20508/ijrer.v2i3.269.g6047
33.
MazouzMSebtiBIlhamiC. DPC- SVM of DFIG using fuzzy second order sliding mode approach. International Journal of Smart Grid-Ijsmartgrid2021; 5(4): 174–182. https://doi.org/10.20508/ijsmartgrid.v5i4.219.g178
34.
AmalorpavarajRAJKaliannanPPadmanabanS, et al.Improved fault ride through capability in DFIG based wind turbines using dynamic voltage restorer with combined feed-forward and feed-back control. IEEE Access2017; 5: 20494–20503. https://doi.org/10.1109/ACCESS.2017.2750738
35.
RayaneLLekhchineS. Fuzzy logic controller-based power control of DFIG based on wind energy systems. International Journal of Smart Grid-ijSmartGrid2024; 8(1): 74–80. https://doi.org/10.20508/ijsmartgrid.v8i1.334.g346
36.
RezaeiNUddinMNAminIK, et al.Genetic algorithm-based optimization of overcurrent relay coordination for improved protection of DFIG operated wind farms. IEEE Trans Ind Appl2019; 55(6): 5727–5736. https://doi.org/10.1109/TIA.2019.2939244
37.
LabdaiSHemiciBNezliL, et al.Control of a DFIG based WECS with optimized PI controllers via a duplicate PSO algorithm. In: 2019 international conference on control, automation and diagnosis (ICCAD). Grenoble, France, July 2-4, 2019, pp. 1–6.
38.
AminIKUddinMN. Nonlinear control operation of DFIG-based WECS incorporated with machine loss reduction scheme. IEEE Trans Power Electron2020; 35(7): 7031–7044. https://doi.org/10.1109/TPEL.2019.2955021
39.
EvangelistaCValenciagaFPulestonP. Active and reactive power control for wind turbine based on a MIMO 2-Sliding mode algorithm with variable gains. IEEE Trans Energy Convers2013; 28(3): 682–689. https://doi.org/10.1109/TEC.2013.2272244
40.
MohamedNAhmedETamouN. Comparative analysis between PI & backstepping control strategies of DFIG driven by wind turbine. Int J Renew Energy Resour2021; 7(3): 1307–1316. https://doi.org/10.20508/ijrer.v7i3.6066.g7163
41.
MohammedFAhmedETamouN. Comparative analysis between robust SMC & conventional PI controllers used in WECS based on DFIG. International Journal of Renewable Energy Research-IJRER2017; 7(4): 2151–2161. https://doi.org/10.20508/ijrer.v7i4.6441.g7267
42.
BenbouhenniHGasmiH. Comparative study of synergetic controller with super twisting algorithm for rotor side inverter of DFIG. International Journal of Smart Grid2022; 6(4): 144–156. https://doi.org/10.20508/ijsmartgrid.v6i4.265.g228
43.
BenbouhenniH. Direct active and reactive powers command with third-order sliding mode theory for DFIG-Based dual-rotor wind power systems. Int J Nat Eng Sci2021; 15(1): 17–34.
44.
MohammedAAhmedETamouN, et al.Comparative analysis of ADRC & PI controllers used in wind turbine system driving a DFIG. Int J Renew Energy Resour2017; 7(4): 1816–1824.
45.
El QouartiOEssadkiALaghridatH, et al.Power control of DFIG based WECS using SOSMC and PSO algorithm. In: 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), Meknes, Morocco, March 3–4, 2022, pp. 1–6.
46.
AhmedM. Comparative study between direct and indirect vector control applied to a wind turbine equipped with a double-fed asynchronous machine article. International Journal of Renewable Energy Research-IJRER2013; 3(1): 88–93. https://doi.org/10.20508/ijrer.v3i1.465.g6109
47.
EchihebFIhedraneYBossoufiB, et al.Robust sliding-backstepping mode control of a wind system based on the DFIG generator. Sci Rep2022; 12: 11782. https://doi.org/10.1038/s41598-022-15960-7
48.
BenbouhenniH. A comparative study between NSMC and NSOSMC strategy for a DFIG integrated into wind energy system. Carpathian Journal of Electronic and Computer Engineering2019; 12(1): 1–8. https://doi.org/10.2478/cjece-2019-0001
49.
de MarchiRADainezPSVon ZubenFJ, et al.A multilayer perceptron controller applied to the direct power control of a doubly fed induction generator. IEEE Trans Sustain Energy2014; 5(2): 498–506. https://doi.org/10.1109/TSTE.2013.2293621
50.
SaihiLBakouYHarrouzA, et al.Fuzzy-sliding mode control second order of wind turbine based on DFIG. In: 2022 10th international conference on smart grid (ICSmartGrid). Istanbul, Turkey, June 27-29, 2022, pp. 296–300.
51.
BenbouhenniHLemdaniS. Combining synergetic control and super twisting algorithm to reduce the active power undulations of doubly fed induction generator for dual-rotor wind turbine system. Electr Eng Electromechanics2021; 2021(3): 8–17. https://doi.org/10.20998/2074-272X.2021.3.02
52.
FaridaMBelkacemSColakI. Fuzzy high order sliding mode control based DPC of DFIG using SVM. In: 2021 9th international conference on smart grid (icSmartGrid). Setubal, 2021, pp. 278–282.
53.
DjilaliLBadillo-OlveraAYuliana RiosY, et al.Neural high order sliding mode control for doubly fed induction generator based wind turbines. IEEE Latin America Transactions2022; 20(2): 223–232. https://doi.org/10.1109/TLA.2022.9661461
54.
SamiIUllahSAminSU, et al.Convergence enhancement of super-twisting sliding mode control using artificial neural network for DFIG-Based wind energy conversion systems. IEEE Access2022; 10: 97625–97641. https://doi.org/10.1109/ACCESS.2022.3205632
55.
PichanMRastegarHMonfaredM. Fuzzy-based direct power control of doubly fed induction generator-based wind energy conversion systems. In: 2012 2nd international eConference on computer and knowledge engineering (ICCKE), Mashhad, Iran, 18-19 October 2012, pp. 66–70.
56.
AmraneFChaibaA. A novel direct power control for grid-connected doubly fed induction generator based on hybrid artificial intelligent control with space vector modulation. Rev. Roum. Sci. Techn.-Electrotechn. Et Energ2016; 61(3): 263–268.
57.
BengourinaMRRahliMSlamiS, et al.PSO based direct power control for a multifunctional grid connected photovoltaic system. Int J Power Electron Drive Syst2018; 9(2): 610–621. https://doi.org/10.11591/ijpeds.v9.i2.pp610-621
58.
ZhangYJiaoJXuD. Direct power control of doubly fed induction generator using extended power theory under unbalanced network. IEEE Trans Power Electron2019; 34(12): 12024–12037. https://doi.org/10.1109/TPEL.2019.2906013
59.
EjlaliAArab khaburiD. Power quality improvement using nonlinear-load compensation capability of variable speed DFIG based on DPC-SVM method. In: The 5th annual international power electronics, drive systems and technologies conference (PEDSTC 2014), Tehran, Iran, 5-6 February 2014, pp. 280–284.
60.
OprinaGChihaiaRAEl-LeatheyLA, et al.A review on counter-rotating wind turbines development. J. Sustain. Energy2016; 7: 91–98.
61.
WangKLiuTWanY, et al.Numerical investigation on aerodynamic characteristics of dual-rotor wind turbines. J Mar Sci Eng2022; 10(1887): 1887. https://doi.org/10.3390/jmse10121887
62.
SaulescuRNeagoeMJaliuC, et al.A comparative performance analysis of counter-rotating dual-rotor wind turbines with speed-adding increasers. Energies2021; 14(2594): 2594. https://doi.org/10.3390/en14092594
63.
ZhamalovAZObozovADKunelbaevMM, et al.Capacity and power characteristics of disk generator with counter-rotation of double-rotor wind turbine. Middle East J Sci Res2013; 15: 1655–1662.
64.
YaichiISemmahAWiraP, et al.An improved direct power control based on SVM strategy of the doubly fed induction generator. 2019 7th international renewable and sustainable energy conference (IRSEC). Agadir, Morocco, 2019, pp. 1–8.
65.
Van NgoBQRodriguez-AyerbePOlaruS. Model predictive direct power control for doubly fed induction generator based wind turbines with three-level neutral-point clamped inverter. In: Iecon 2016 - 42nd annual conference of the IEEE industrial electronics society. IEEE, 2016, pp. 3476–3481.
66.
HabibBBoudjemaZBelaidiA. Power control of DFIG in WECS using DPC and NDPC-NPWM methods. Mathematical Modelling of Engineering Problems2020; 7(2): 223–236.
HabibB. Application of STA methods and modified SVM strategy in direct vector control system of ASG integrated to dual-rotor wind power: simulation studies. International Journal of Smart Grid2021; 5(1): 62–72.
69.
XiongPSunD. Backstepping-Based DPC strategy of a wind turbine-driven DFIG under normal and harmonic grid voltage. IEEE Trans Power Electron2016; 31(6): 4216–4225. https://doi.org/10.1109/TPEL.2015.2477442
70.
BenbouhenniH. Synergetic control theory scheme for asynchronous generator based dual-rotor wind power. Journal of Electrical Engineering, Electronics, Control and Computer Science2021; 7(3): 19–28.
71.
ChatterjeeKKumarA. Contribution to power quality improving the DFIG control using a reduced components of multi-level inverter. In: 2021 10th IEEE international conference on communication systems and network technologies (CSNT). IEEE, 2021, pp. 291–294.
72.
HuJNianHHuB, et al.Direct active and reactive power regulation of DFIG using sliding-mode control approach. IEEE Trans Energy Convers2010; 25(4): 1028–1039. https://doi.org/10.1109/TEC.2010.2048754
73.
MohammadiJVaez-ZadehSAfsharniaS, et al.A combined vector and direct power control for DFIG-Based wind turbines. IEEE Trans Sustain Energy2014; 5(3): 767–775. https://doi.org/10.1109/TSTE.2014.2301675
74.
KamelDEAbdelkaderMLarbiB, et al.A comprehensive review of LVRT capability and sliding mode control of grid-connected wind-turbine-driven doubly fed induction generator. Automatika2016; 57(4): 922–935. https://doi.org/10.7305/automatika.2017.05.1813
75.
YaichiISemmahAWiraP, et al.Super-twisting sliding mode control of a doubly-fed induction generator based on the SVM strategy. PeriodicaPolytechnica Electrical Engineering and Computer Science2019; 63(3): 178–190. https://doi.org/10.3311/ppee.13726
76.
AyriraWOurahouaMEl HassouniaB, et al.Direct torque control improvement of a variable speed DFIG based on a fuzzy inference system. Math Comput Simulat2020; 167: 308–324. https://doi.org/10.1016/j.matcom.2018.05.014
77.
YahdouAHemiciBBoudjemaZ. Second order sliding mode control of a dual-rotor wind turbine system by employing a matrix converter. J Electr Eng2016; 16(3): 1–11.
78.
QuanYHangLHeY, et al.Multi-Resonant-based sliding mode control of DFIG-Based wind system under unbalanced and harmonic network conditions. Appl. Sci2019; 9: 1124. https://doi.org/10.3390/app9061124
