This paper presents the modeling of wind energy systems using MATLAB Simulink. The model considers the MPPT (Maximum Power Point Tracking) technique to track the maximum power that could be extracted from the wind energy. The main contribution of our work lines in the choice of the Wind Energy Conversion System (WECS) components and the suitable MPPT technique. An innovating maximum MPPT technique is proposed. This technique combines the advantages of an ameliorated Perturb & Observe (P&O) control algorithm and the speed of Optimal Relationship Based (ORB) control algorithm to allow the MPPT controller output (duty cycle) adjusts the voltage input of a Buck-Boost converter to track the maximum power point. It searches for the system optimum relationship for maximum power point tracking and then controls the system based on this relationship. The main advantage of the proposed technique is that it does not require an anemometer or preknowledge of a system, but has an accurate and fast response to wind speed fluctuations. The MATLAB/Simulink simulation results show that the developed model components choice complies with cost and reliability constraints and confirm the validity and performance of the proposed control algorithm.
AgarwalVAggarwalRKPatidarP, et al. (2010) A novel scheme for rapid tracking of maximum power point in wind energy generation systems. IEEE Transactions on Energy Conversion25: 228–236.
2.
BalasundarCSudharshananSElakkiyavendanR (2015) Design of an optimal tip speed ratio control MPPT algorithm for standalone WECS. International Journal for Research in Applied Science and Engineering Technology3: 442–450.
3.
DalalaZMZahidZUYuW, et al. (2013) Design and analysis of an MPPT technique for small-scale wind energy conversion systems. IEEE Transactions on Energy Conversion28: 756–767.
4.
GraggerJVHaumerAEinhornM (2010) Averaged model of a buck converter for efficiency analysis. Engineering Letter18(1): 1–6.
5.
HaqueMENegnevitskyMMuttaqiKM (2010) A novel control strategy for a variable-speed wind turbine with a permanent-magnet synchronous generator. IEEE Transactions on Industry Applications46: 331–339.
6.
HeydariMSmedleyK (2015) Comparison of maximum power point tracking methods for medium to high power wind energy systems. In: Proceedings of 2015 20th conference on electrical power distribution networks conference (EPDC), Zahedan, Iran, pp.184–189. New York, NY: IEEE.
7.
HuangCLiFJinZ (2015) Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics. IEEE Transactions on Industrial Electronics62(4): 2530–2539.
8.
MahdavianMWattanapongsakornNShahgholianG, et al. (2014) Maximum power point tracking in wind energy conversion systems using tracking control system based on fuzzy controller. In: Proceedings of 2014 11th inter-national conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), Nakhon Ratchasima, Thailand.
9.
MarouaniHSouhirT (2014) Design of a coiled rotor synchronous motor dedicated to electric traction. Journal of Electrical Systems10(3): 344–358.
10.
MireckiA (2005) Etude comparative de chaines de conversion d’énergiedédiees ‘a une éolienne de petite puissance. PhD Thesis.
11.
NasiriMMilimonfaredJFathiSH (2014) Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines. Energy Conversion and Management86: 892–900.
12.
NasriNTounsiS (2015) Design and optimization of renewable energy structure. Journal of Electrical Engineering7(8): 1–13.
13.
NejiRTounsiSSellamiF (2006) Contribution à la définition d’un moteur à aimant permanent avec un coût de production réduit pour la propulsion du véhicule électrique». Transactions européennes sur l’énergie électrique (ETEP)16: 437–460.
14.
PertuzaC (1996) Contribution à la définition de moteurs à aimants permanents pour un véhicule électrique routier. Thèse de docteur de l’Institut National Polytechnique de Toulouse, Février.
15.
RahimAHMA (2014) Optimum relation based maximum power point tracking of a PMSG wind generator through converter controls. In: Proceedings of the 7th IET international conference on power electronics, machines and drives (PEMD 2014), Manchester, pp.1–6. New York, NY: IEEE.
16.
SinghGMatuontoMAmosJ, et al. (2019) System of systems strand tilt analysis perspective on MediuMHigh voltage stator bar and a non-destructive testing case study. In: 2019 IEEE international systems conference (SysCon), Orlando, FL, 8–11 April 2019. DOI: 10.1109/SYSCON.2019.8836934.
17.
SinghGMatuontoMSundaramK (2019) Impact of imbalanced wind turbine generator cooling on reliability. In: 2019 10th international renewable energy congress (IREC), Sousse, Tunisia, 26–28 March 2019. DOI: 10.1109/IREC.2019.8754545.
18.
SinghGSundaramK (2021) Manufacturing deformation impact on the performance of electrical generator for the wind turbine application. Wind Engineering45(5): 1193–1205.
19.
SinghGSundaramK (2022) Methods to improve wind turbine generator bearing temperature imbalance for onshore wind turbines. Wind Engineering46: 150–159.
20.
SinghGSundaramKMatuontoM (2021) A solution to reduce overheating and increase wind turbine systems availability. Wind Engineering45(3): 491–504.
21.
SinghGSundaramKSalehA (2019) Addressing reduced ingress protection class & proper filter selection for open ventilated (IC3A1) wind turbine generator. In: 2019 10th international renewable energy congress (IREC), Sousse, Tunisia, 26–28 March 2019. DOI: 10.1109/IREC.2019.8754542.
22.
SinghMChandraA (2011) Application of adaptive network-based fuzzy inference system for sensorless control of PMSG-Based wind turbine with nonlinear-load-compensation capabilities. IEEE Transactions on Power Electronics26: 165–175.
23.
SuilahANadiaGBoudayaA, et al. (2015) Modelling of synchronous generation system for renewable energy. International Journal of Electrical Components and Energy Conversion1(1): 36–43.
24.
TanKIslamS (2004) Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. IEEE Transactions on Energy Conversion19: 392–399.
25.
TounsiS (2015a) Systemic design and optimization improving performances of permanent magnet motors. International Journal of Electrical Components and Energy Conversion1(2): 1–15.
26.
TounsiS (2015b) Design and optimization of axial flux brushless DC generator dedicated to generation of renewable energy. American Journal of Electrical Power and Energy Systems4(3): 1–5.
27.
TounsiSNejiRSellamiF (2006) Contribution à la conception d’un actionneur à aimants permanents pour véhicules électriques en vue d’optimiser l’autonomie. Revue Internationale de Génie Electrique9(6): 693–718.
28.
WeiCZhangZQiaoW, et al. (2016) An adaptive network-based reinforcement learning method for MPPT control of PMSG wind energy conversion systems. IEEE Transactions on Power Electronics31(11): 7837–7848.
