The increase of the favorable impacts of wind energy on the environment and the global energy requires overall understanding of the modeling methods that are commonly used for time-based simulation of wind energy systems. This paper introduces a comprehensive comparison of three salient modeling techniques of wind energy conversion systems, which are: the physical modeling, subspace system identification, and Dynamic Neural Network (ANN). The models have been created with the different modeling philosophies with the aid of historical data-sets representing four apart days of operation. The real system incorporates (TWT-1.65) type Wind-Turbine intergated with Multi-Pole Synchronous Generators (MPSG). The compariosn provides some crucial answers to the concerns of which technique is suited for an application, consequently, the comparison includes quantitative and qualitative measures. This article can be considered as a brief guide for future researchers to have thorough understanding of the modeling concepts in the field of wind engineering.
AhmadABMohamedOR (2019) Reduction of fluctuation of wind turbines’ output power by modeling and control. International Review of Electrical Engineering (IREE)14(4): 272–283.
2.
AliRBSchulteHMamiA (2017) Modeling and simulation of a small wind turbine system based on PMSG generator. In: 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, Ljubljana, Slovenia, pp.1–6.
3.
BakriAEKoumirMBoumhidiI (2016) Extreme learning machine for fault detection and isolation in wind turbine. In: 2016 international conference on electrical and information technologies (ICEIT), Tangiers, Morocco, pp.174–179. IEEE.
4.
ChenXHaoWJQiaoY, et al. (2018) Identification of wind turbine model by subspace method and prediction error method. In: 2018 Chinese control and decision conference (CCDC), Shenyang, China, pp.4609–4614. IEEE.
5.
ChuJYuanLHuY, et al. (2019) Comparative analysis of identification methods for mechanical dynamics of large-scale wind turbine. Energies12(18): 3429.
6.
HeXGengHMuG (2021) Modeling of wind turbine generators for power system stability studies: A review. Renewable and Sustainable Energy Reviews143: 110865.
LabidiZRSchulteHMamiA (2017) Modeling and optimal torque control of small wind turbines with permanent magnet synchronous generators. In: 2017 international conference on green energy conversion systems (GECS), Hammamet, Tunisia, pp.1–6. IEEE.
9.
NielsonJBhaganagarKMekaR, et al. (2020) Using atmospheric inputs for artificial neural networks to improve wind turbine power prediction. Energy190: 116273.
10.
QataminRMohamedORIAbu ElhaijaWS (2020) Prediction of power output of wind turbines using system identification techniques. International Review on Modelling and Simulations (IREMOS)13(1): 43–51.
11.
RolanALunaAVazquezG, et al. (2009) Modeling of a variable speed wind turbine with a permanent magnet synchronous generator. In: 2009 IEEE international symposium on industrial electronics, Seoul, Korea (South), pp.734–739. IEEE.
12.
ShairJXieXLiuW, et al. (2021) Modeling and stability analysis methods for investigating subsynchronous control interaction in large-scale wind power systems. Renewable and Sustainable Energy Reviews135: 110420.
13.
SinghGLentijoSSundaramK (2019a) The impact of the converter on the reliability of a wind turbine generator. In: Proceedings of the ASME 2019 power conference, Salt Lake City, UT, USA, 15–18 July 2019. ASME.
14.
SinghGMatuontoMAmosJ, et al. (2019b) System of systems strand tilt analysis perspective on medium high voltage stator bar and a non-destructive testing case study. In: 2019 IEEE international systems conference (SysCon), Orlando, FL, USA, 8–11 April 2019, pp.1–8. New York, NY: IEEE.
15.
SinghGMatuontoMSundaramK (2019c) Impact of imbalanced wind turbine generator cooling on reliability. In: 2019 10th international renewable energy congress (IREC), Sousse, Tunisia, 26–28 March 2019, pp.1–6. New York, NY: IEEE.
16.
SinghGSundaramKSalehA (2019d) Addressing reduced ingress protection class and proper filter selection for open ventilated (IC3A1) wind turbine generator. In: 2019 10th international renewable energy congress (IREC), Sousse, Tunisia, 26–28 March 2019, pp.1–5. New York, NY: IEEE.
17.
SinghGSalehAAmosJ, et al. (2018) IC6A1A6 vs. IC3A1 squirrel cage induction generator cooling configuration challenges and advantages for wind turbine application. In: Proceedings of the ASME 2018 power conference collocated with the ASME 2018 12th international conference on energy sustainability and the ASME 2018 nuclear forum. Volume 1: Fuels, combustion, and material handling; combustion turbines combined cycles; boilers and heat recovery steam generators; virtual plant and cyber-physical systems; plant development and construction; renewable energy systems. Lake Buena Vista, FL, USA, 24–28 June 2018.
18.
SinghGSundaramK (2021a) Common mode current effects and challenges for wind turbine generator application. In: Proceedings of the ASME 2021 power conference. Virtual, online, 20–22 July 2021. ASME.
19.
SinghGSundaramK (2021b) Manufacturing deformation impact on the performance of electrical generator for the wind turbine application. Wind Engineering45(5): 1193–1205.
20.
SinghGSundaramK (2021c) Methods to improve wind turbine generator bearing temperature imbalance for onshore wind turbines. Wind Engineering. Epub ahead of print 13May2021. DOI: 10.1177/0309524X211015292
21.
SinghGSundaramKMatuontoM (2021) A solution to reduce overheating and increase wind turbine systems availability. Wind Engineering45(3): 491–504.
22.
StaffellIJansenMChaseA, et al. (2018) Energy Revolution: Global Outlook. Drax: Selby.
23.
TorkiWGrouzFSbitaL (2017) A sliding mode model reference adaptive control of PMSG wind turbine. In: 2017 international conference on green energy conversion systems (GECS), Hammamet, Tunisia, pp.1–6. IEEE.
24.
VadiSGürbüzFBBayindirR, et al. (2020) Design and simulation of a grid connected wind turbine with permanent magnet synchronous generator. In: 2020 8th international conference on smart grid (icSmartGrid), Paris, France, pp.169–175. IEEE.
25.
VeenaRFeminVMathewS, et al. (2016) Intelligent models for the power curves of small wind turbines. In: 2016 international conference on cogeneration, small power plants and district energy (ICUE), Bangkok, Thailand, pp.1–5. IEEE.
26.
VerhaegenM (1994) Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica30(1): 61–74.
