In the present study 5MW NREL horizontal axis wind turbine (HAWT) has been considered for pitch control to reduce fatigue loads and maintain rated power in region III of operation, that is, above rated wind condition. An electrohydraulic linear actuation system has been considered for blade-pitch revolve-motion of the rotor blade, based upon available wind speed. An augmented state improved exponential reaching law integral sliding mode controller (ASIERL-ISMC) has been designed for the HAWT pitch control application. In contrast to the previously reported improved exponential reaching law based controllers, in our ASIERL-ISMC, all state variables and the sliding variable have been considered together to define an augmented reaching law that can enhance the degree of freedom of the controller. Further, hyperparameters of the proposed ASIERL-ISMC have been optimized using Harris hawks optimization (HHO), to achieve superior control performance. The performances of this proposed HHO-ASIERL-ISMC have been compared with existing dynamic exponential term reaching law integral sliding mode controller (DETRL-ISMC) and improved exponential reaching law integral sliding mode controller (IERL-ISMC). The robustness and sensitivity of the proposed ASIERL-ISMC and HHO-ASIERL-ISMC have also been tested with three different available wind data. The proposed HHO-ASIERL-ISMC has been found to provide the most superior performance, in terms of both blade-pitch angle error (computed using integral absolute error (IAE)) and control energy (CE).
CivelekZ.Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Eng Sci Technol Int J2020; 23(1): 1–9.
2.
AsgharniaAShahnaziRJamaliA.Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Trans2018; 79: 27–44.
3.
EkinciFDemirdelenTAksuIO, et al. A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine. Proc IMechE, Part A: J Power and Energy2019; 233(5): 646–658.
4.
VenkaiahPSarkarBK.Electrohydraulic proportional valve-controlled vane type semi-rotary actuated wind turbine control by feedforward fractional-order feedback controller. Proc IMechE, Part I: J Systems and Control Engineering2022; 236(2): 318–337.
5.
MalekiGHDavariARSoltaniMR.Impact of dielectric barrier discharge plasma on the wake of a wind turbine blade section oscillating in plunge. Proc IMechE, Part A: J Power and Energy2022; 236(2): 320–335.
6.
SenjyuTSakamotoRUrasakiN, et al. Output power control of wind turbine generator by pitch angle control using minimum variance control. Elect Eng Jpn2006; 154(2): 10–18.
7.
AbolvafaeiMGanjefarS.Two novel approaches to capture the maximum power from variable speed wind turbines using optimal fractional high-order fast terminal sliding mode control. Eur J Control2021; 60: 78–94.
8.
AbolvafaeiMGanjefarS.Maximum power extraction from a wind turbine using second-order fast terminal sliding mode control. Renew Energy2019; 139: 1437–1446.
9.
KumarNVenkaiahPSarkarBK, et al. Wind turbine electrohydraulic transmission system control for maximum power tracking with pump fault. Proc IMechE, Part I: J Systems and Control Engineering. Epub ahead of print 21 February 2023. DOI: 10.1177/09596518231155
10.
PoultangariIShahnaziRSheikhanM.RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm. ISA Trans2012; 51(5): 641–648.
11.
JelavićMPetrovićVPerićN.Estimation based individual pitch control of wind turbine. Autom2010; 51(2): 181–192.
12.
VenkaiahPSarkarBK.Hydraulically actuated horizontal axis wind turbine pitch control by model free adaptive controller. Renew Energy2020; 147: 55–68.
13.
YinXZhangWJiangZ, et al. Adaptive robust integral sliding mode pitch angle control of an electro-hydraulic servo pitch system for wind turbine. Mech Syst Signal Process2019; 133: 105704.
14.
YinXXLinYGLiW, et al. Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines. ISA Trans2015; 58: 629–634.
15.
HosseiniEAghadavoodiEFernández RamírezLM.Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning. Renew Energy2020; 157: 897–910.
16.
BossanyiEA.Individual Blade pitch control for load reduction. Wind Energy2003; 6(2): 119–128.
17.
AssarehEBiglariM.A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm. Renew Sustain Energ Rev2015; 51: 1023–1037.
18.
SinghNPratapBSwarupA.Robust control design of variable speed wind turbine using quantitative feedback theory. Proc IMechE, Part A: J Power and Energy2018; 232(6): 691–705.
19.
LeitheadWEConnorB.Control of variable speed wind turbines: design task. Int J Control2000; 73(13): 1189–1212.
20.
JiaCWangLMengE, et al. Combining LIDAR and LADRC for intelligent pitch control of wind turbines. Renew Energy2021; 169: 1091–1105.
21.
WangNJohnsonKEWrightAD.FX-RLS-based feedforward control for LIDAR-enabled wind turbine load mitigation. IEEE Trans Control Syst Technol2012; 20(5): 1212–1222.
22.
ZhangDCrossPMaX, et al. Improved control of individual blade pitch for wind turbines. Sens Actuators A Phys2013; 198: 8–14.
23.
ChiangMH.A novel pitch control system for a wind turbine driven by a variable-speed pump-controlled hydraulic servo system. Mechatronics2011; 21(4): 753–761.
24.
DuongMQGrimacciaFLevaS, et al. Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system. Renew Energy2014; 70: 197–203.
25.
ChowdhuryMAHosseinzadehNShenWX.Smoothing wind power fluctuations by fuzzy logic pitch angle controller. Renew Energy2012; 38(1): 224–233.
26.
JafarnejadsaniHPieperJEhlersJ.Adaptive control of a variable-speed variable-pitch wind turbine using radial-basis function neural network. IEEE Trans Control Syst Technol2013; 21(6): 2264–2272.
27.
BehroozFMariunNMarhabanM, et al. Review of control techniques for HVAC systems—nonlinearity approaches based on fuzzy cognitive maps. Energies2018; 11(3): 495.
28.
HassanHMElShafeiALFaragWA, et al. A robust LMI-based pitch controller for large wind turbines. Renew Energy2012; 44: 63–71.
29.
LiuYWangZXiongL, et al. DFIG wind turbine sliding mode control with exponential reaching law under variable wind speed. Int J Electr Power Energy Syst2018; 96: 253–260.
30.
FallahaCJSaadMKanaanHY, et al. Sliding-mode robot control with exponential reaching law. IEEE Trans Ind Electron2011; 58(2): 600–610.
31.
XuQSongJLYanTH, et al. A Novel Exponential reaching law for sliding mode control of discrete-time system with disturbance. In: 2018 Australian & New Zealand control conference (ANZCC), Melbourne, VIC, 2018, pp.275–280. New York, NY: IEEE.
32.
GaoWHungJC.Variable structure control of nonlinear systems: a new approach. IEEE Trans Ind Electron1993; 40(1): 45–55.
33.
WhiteWNYuZLuceroC. Active damping of torsional resonance in wind turbine drivetrains. In: IECON 2014 – 40th annual conference of the IEEE Industrial Electronics Society, Dallas, TX, 2014, pp.1957–63. New York, NY: IEEE.
34.
WangAWeiS.Sliding mode control for permanent magnet synchronous motor drive based on an improved exponential reaching law. IEEE Access2019; 7: 146866–146875.
35.
MercorelliPWernerN.Integrating a piezoelectric actuator with mechanical and hydraulic devices to control camless engines. Mech Syst Signal Process2016; 78: 55–70.
36.
CarchraeTBeckJC.Applying machine learning to low-knowledge control of optimization algorithms. Comput Intell2005; 21(4): 372–387.
37.
MirMDayyaniMSutiknoT, et al. Employing a gaussian particle swarm optimization method for tuning multi input multi output-fuzzy system as an integrated controller of a micro-grid with stability analysis. Comput Intell2020; 36(1): 225–258.
38.
MandavaSMedarametlaPKGudipalliA, et al. Integration of AI with reduced order generalized integrator controller for power system harmonic reduction. Comput Intell2021; 37(3): 1068–1079.
39.
SarkarBKMandalPSahaR, et al. GA-optimized feedforward-PID tracking control for a rugged electrohydraulic system design. ISA Trans2013; 52(6): 853–861.
40.
MandalPSarkarBKSahaR, et al. GA-optimized fuzzy-feedforward-bias control of motion by a rugged electrohydraulic system. IEEE ASME Trans Mechatron2015; 20(4): 1734–1742.
41.
MandalPSarkarBKSahaR, et al. Real-time fuzzy-feedforward controller design by bacterial foraging optimization for an electrohydraulic system. Eng Appl Artif Intell2015; 45: 168–179.
42.
Barrios AguilarMECouryDVReginattoR, et al. Multi-objective PSO applied to PI control of DFIG wind turbine under electrical fault conditions. Elect Power Syst Res2020; 180: 106081.
43.
DubeyHMPanditMPanigrahiBK.Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. Int J Electr Power Energy Syst2016; 83: 158–174.
44.
BakirHMerabetADharRK, et al. Experimental evaluation of water cycle technique for control parameters optimization of double-fed induction generator-based wind turbine. Eng Sci Technol Int J2021; 24(4): 890–898.
45.
Fernandez-GaunaBGrañaMOsa-AmilibiaJL, et al. Actor-critic continuous state reinforcement learning for wind-turbine control robust optimization. Inf Sci2022; 591: 365–380.
46.
HeidariAAMirjaliliSFarisH, et al. Harris hawks optimization: algorithm and applications. Future Gener Comput Syst2019; 97: 849–872.
47.
ChenHHeidariAAChenH, et al. Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Future Gener Comput Syst2020; 111: 175–198.
48.
JiaHLangCOlivaD, et al. Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens2019; 11(12): 1421.
49.
SongSWangPHeidariAA, et al. Dimension decided Harris hawks optimization with Gaussian mutation: balance analysis and diversity patterns. Knowl-Based Syst2021; 215: 106425.
50.
VasquezSKinnaertMPintelonR.Active fault diagnosis on a hydraulic pitch system based on frequency-domain identification. IEEE Trans Control Syst Technol2019; 27(2): 663–678.
51.
DasmahapatraSSarkarBKSahaR, et al. Design of an adaptive fuzzy-bias SMC and validation for a rugged electrohydraulic system. IEEE ASME Trans Mechatron2015; 20(6): 2708–2715.
52.
VenkaiahPSarkarBKChaterjeeA.Pitch control of electrohydraulic semi-rotary-actuated wind turbine with actuator and valve fault using generalized power-based exponential rate reaching law sliding mode controller. Proc IMechE, Part I: J Systems and Control Engineering2023. 237(8): 1423–1439
53.
RoyRMukherjeeVSinghRP.Harris hawks optimization algorithm for model order reduction of interconnected wind turbines. ISA Trans2022; 128: 372–385.
54.
HussienAGAminM.A self-adaptive Harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. Int J Mach Learn Cybern2022; 13(2): 309–336.
55.
DasSMullickSSSuganthanPN.Recent advances in differential evolution – an updated survey. Swarm Evol Comput2016; 27: 1–30.
56.
LanJPattonRJZhuX.Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation. Renew Energy2018; 116: 219–231.
57.
GolnaryFTseKT.Novel sensorless fault-tolerant pitch control of a horizontal axis wind turbine with a new hybrid approach for effective wind velocity estimation. Renew Energy2021; 179: 1291–1315.
58.
GanjefarSMohammadiA.Variable speed wind turbines with maximum power extraction using singular perturbation theory. Energy2016; 106: 510–519.
59.
JonkmanJButterfieldSMusialW, et al. Definition of a 5-MW Reference wind turbine for Offshore system development. Technical report, NREL, Golden, CO, 2009, https://www.nrel.gov/docs/fy09osti/38060.pdf
