The work presented in this paper deals with an AC model of wind turbine system conversion to a DC model with reduced simulation time, for possible integration to optimization software with larges scales permitting a multi-objective optimization, such as the constrained optimization conjointly of the cost and power losses of the wind turbine energy system. The DC model is based on average calculation of the DC voltage recharging the battery energy accumulator used for recovering the converted wind energy and the electromagnetic torque. Indeed, classical model of wind turbine using generally electric generator associated to a PD3 rectifier to convert the alternative energy on DC energy recoverable on battery energy accumulator with need a large simulation time and thereafter it is non integrable to optimization software for multi-objective optimization problem resolution. The two models are implemented under Matlab-Simulink simulation environment. Simulation results valid entirely the wind turbine system DC model. Finally, as perspective it is interesting to use a booster chopper as an interface between the rectifier and the battery to optimize the recovered energy. An average model of the booster chopper can be integrated into the DC model for performance improvement of the conversion chain.
AliMASMahmoodKKBalochS, et al. (2020a) Wind-speed estimation and sensorless control for SPMSG-based WECS using LMIbased SMC. IEEE Access8: 26524–26535.
2.
AliMNMahmoudKLehtonenM, et al. (2021) An efficient fuzzy-logic based variable-step incremental conductance MPPT method for grid-connected PV systems. IEEE Access9: 26420–26430.
3.
AliAMahmoudKRaiszD, et al. (2020b) Optimal allocation of inverter-based WTGS complying with their DSTATCOM functionality and PEV requirements. IEEE Trans Veh Technol69(5): 4763–4772.
4.
AlyMAhmedEMShoyamaM (2017) Thermal and reliability assessment for wind energy systems with DSTATCOM functionality in resilient microgrids. IEEE Trans Sustain Energ8(3): 953–965.
5.
BeltranBAhmed-AliTBenbouzidMEH (2008) Sliding mode power control of variable-speed wind energy conversion systems. IEEE Trans Energy Convers23(2): 551–558.
6.
BhattacharyyaTChatterjeeBSinghPK, et al. (2020) Mayfly in harmony: A new hybrid meta-heuristic feature selection algorithm. IEEE Access8: 195929–195945.
7.
ChengFQuLQiaoW (2018) Fault prognosis and remaining useful life prediction of wind turbine gearboxes using current signal analysis. IEEE Trans Sustain Energy9(1): 157–167.
8.
CrossSHastAKuhi-ThalfeldtR, et al. (2015) Progress in renewable electricity in Northern Europe towards EU 2020 targets. Renew Sustain Energy Rev52: 1768–1780.
9.
DasAKarAKKumarC, et al. (2020) TSBC converter with BESS for DFIG-based wind energy conversion system. IEEE Trans Ind Appl56(6): 6158–6173.
10.
EbrahimMABecherifMAbdelazizAY (2018) Dynamic performance enhancement for wind energy conversion system using moth-flame optimization based blade pitch controller. Sustain Energy Technol Assessments27: 206–212.
11.
EbrahimMAEl-MetwallKABendaryFM, et al. (2012) Optimization of proportional-integral-differential controller for wind power plant using particle swarm optimization technique. Int J Electr Power Eng6(1): 32–37.
12.
EbrahimMAEl-Metwally BendaryFM, et al. (2012) Transient stability enhancement of a wind energy distributed generation system by using fuzzy logic stabilizers. Wind Eng36(6): 687–700.
13.
ElsisiM (2019a) New design of adaptive model predictive control for energy conversion system with wind torque effect. J Clean Prod240: 118265.
14.
ElsisiM. (2019b) Future search algorithm for optimization. Evol Intel12: 21–31.
15.
ElsisiM (2019c) Optimal design of non-fragile PID controller. Asian J Control23(2): 729–738.
16.
ElsisiM (2020) New design of robust PID controller based on meta-heuristic algorithms for wind energy conversion system. Wind Energy23(2): 391–403.
17.
ElsisiMMahmoudKLehtonenM, et al. (2021) An improved neural network algorithm to efficiently track various trajectories of robot manipulator arms. IEEE Access9: 11911–11920.
18.
ElsisiMSolimanMAboelelaMAS, et al. (2015) GSA-based design of dual proportional integral load frequency controllers for nonlinear hydrothermal power system. World Acad Sci Eng Technol9: 1142–1148.
19.
ElsisiMTranM-QMahmoudK, et al. (2021) Robust design of ANFIS-based blade pitch controller for wind energy conversion systems against wind speed fluctuations. IEEE Access9: 37894–37904.
20.
GengHYangG (2014) Linear and nonlinear schemes applied to pitch control of wind turbines. Sci World J2014: 1–9.
21.
HussainJMishraMK (2020) An efficient wind speed computation method using sliding mode observers in wind energy conversion system control applications. IEEE Trans Ind Appl56(1): 730–739.
22.
JangJ-S-R (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern23(3): 665–685.
KhosraviAOlkkonenVFarsaeiA, et al. (2020) Replacing hard coal with wind and nuclear power in Finland-impacts on electricity and district heating markets. Energy203: 117884.
25.
KoutroulisEKalaitzakisK (2006) Design of a maximum power tracking system for wind-energy-conversion applications. IEEE Trans Indus Electr53(2): 486–494.
26.
LiHWuYChenM (2021) Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm. IEEE Trans Cybern51(3): 1163–1174.
27.
LiangHLiuGZhangH, et al. (2021) Neural-network-based eventtriggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties. IEEE Trans Neural Netw Learn Syst32(5): 2239–2250.
28.
LiuHBuFHuangW, et al. (2020) Control winding direct power control strategy for five-phase dual-stator winding induction generator DC generating system. IEEE Trans Transport Electrific6(1): 73–82.
29.
MaYTaoLZhouX, et al. (2020) Analysis and control of fault ridethrough capability improvement for wind energy conversion system using linear active disturbance rejection control with correction link. IEEE Access8: 73816–73827.
30.
ManKFTangKSKwongS (1996) Genetic algorithms: Concepts and applications. IEEE Trans Ind Electron43(5): 519–534.
31.
NejiRTounsiSSellamiF (2006) Contribution to the definition of a permanent magnet motor with reduced production cost for the electrical vehicle propulsion. J Eur Trans Electr Power16(4): 437–460.
32.
NhidiWTounsiSBouhlelMS (2015) Design and modeling of a synchronous renewable energy generation system. Am J Electr Power Energ Syst4(1–3): 6–11.
33.
NikiforowKPennanenJIhonenJ, et al. (2018) Power ramp rate capabilities of a 5 kW proton exchange membrane fuel cell system with discrete ejector control. J Power Sources381: 30–37.
34.
OuyangJTangTYaoJ, et al. (2019) Active voltage control for DFIGbased wind farm integrated power system by coordinating active and reactive powers under wind speed variations. IEEE Trans Energy Convers34(3): 1504–1511.
35.
PanYDuPXueH, et al. (2020) Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance. IEEE Trans Fuzzy Syst. Epub ahead of print 3June2020. DOI:10.1109/TFUZZ.2020. 2999746.
36.
PrzybylekPGielniakJ. (2019) Analysis of gas generated in mineral oil, synthetic ester, and natural ester as a consequence of thermal faults. IEEE Access7: 65040–65051.
37.
SouedSEbrahimMARamadanHS, et al. (2017) Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers. IET Electr Power Appl11(8): 1432–1440.
38.
SugenoMTanakaK (1991) Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets Syst42(3): 315–334.
