Abstract
The system under investigation is a wind turbine of 5 MW connected via a gearbox to a doubly-fed induction generator (DFIG). The stator of this DFIG is connected directly to the grid, while the rotor uses back-to-back converters to connect to the grid. This chapter focuses on controlling the active and reactivepower generated by a variable wind power plant and the power transferred between the electrical grid and the system. A maximum power point tracking (MPPT) technique is also utilized to get the maximum power of the fluctuating wind speed. The rotor side converter (RSC) and grid side converter (GSC) decoupled vector control is principally established by a traditional Proportional-Integral (PI) and with an intelligent PI whose parameters are modified using the particle swarm optimization technique (PSO). Through Matlab/Simulink, the performances and results obtained by classical PI are studied and compared to those obtained by PSO tuned PI controller.
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References
Alhato MM, Bouallègue S (2019) Direct power control optimization for doubly fed induction generator based wind turbine systems. Math Comput Appl 24(3):77
Bouazza H, Bendaas ML, Allaoui T, Denai M (2020) Application of artificial intelligence to wind power generation: modelling, control and fault detection. Int J Intell Syst Technol Appl 19(3):280–305
Oliveira CMR, Aguiar ML, Monteiro JRBA, Pereira WCA, Paula GT, Almeida TEP (2016) Vector control of induction motor using an integral sliding mode controller with anti-windup. J Control Autom Electr Syst 27(2):169–178
Arama FZ, Bousserhane IK, Laribi S, Sahli Y, Mazari B (2018) Artificial intelligence control applied in wind energy conversion. System 9(2):571–578
Muthusamy M, Parvathy AK (2020) Artificial intelligence techniques-based low voltage ride through enhancement of DFIG. J Mech Continua Math Sci 15(3):125–139
Nassimuallah, Irfan S, Chowdhury MDS, Techato K, Alkhammash H (2017) Artificial intelligence integrated fractional order control for Doubly Fed induction Generator based wind energy system. IEEE Access 04
Ben Belghith O, Sbita L, Bettaher F (2016) MPPT design using PSO technique for photovoltaic system control comparing to fuzzy logic and P&O controllers. Energy Power Eng 08:349–366
Bharti OP, Saket RK, Nagar SK (2017) Controller design for doubly fed induction generator using particle swarm optimization technique. Renew Energy 114:1394–1406
Baba AO, Liu G, Chen X (2020) Classification and evaluation review of maximum power point tracking methods. Sustain Futures 2(February)
El Filali A, Zazi M (2021) Arduino implementation of MPPT with P and O algorithm in photovoltaic systems. Int J Eng Appl Phys (IJEAP) 1(1):9–17
Chetouani E, Errami Y, Obbadi A, Sahnoun S (2021) Backstepping and indirect vector control for rotor side converter of Doubly Fed-induction generator with maximum power point tracking. In: Motahhir S, Bossoufi B (eds) Digital technologies and applications. ICDTA 2021, Lecture Notes in Networks and Systems, vol 211, pp 1711–1723
Errami Y, Obbadi A, Sahnoun S (2020) Control of PMSG wind electrical system in network context and during the MPP tracking process. Int J Syst Control Commun 11(2):200–225
Ihedrane Y, El Bekkali C, Bossoufi B, Bouderbala M (2019) Control of power of a DFIG generator with MPPT technique for wind turbines variable speed. In: Derbel N, Zhu Q (eds) Modeling, identification and control methods in renewable energy systems, green energy and technology. Springer, Singapore, pp 105–129
Elazzaoui M (2015) Mode modeling and control of a wind system based Doubly Fed induction generator: optimization of the power produced. J Electr Electron Syst 4(1):1–8
Dahbi A, Nait-Said N, Nait-Said M (2016) A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network. Int J Hydrogen Energy 41(22):9427–9442
Bouderbala M, Bossoufi B, Lagrioui A, Taoussi M, Alami H, Ihedrane Y (2018) Direct and indirect vector control of a doubly fed induction generator based in a wind energy conversion system. Int J Electr Comput Eng (IJECE) 9(3):1531–1540
Laina R, Lamzouri F, Boufounas E, El Amrani A, Boumhidi I (2018) Intelligent control of a DFIG wind turbine using a PSO evolutionary algorithm. Procedia Comput Sci 127(2018):471–480
Chetouani E, Errami Y, Obbadi A, Sahnoun S (2021) Optimal tuning of PI controllers using adaptive particle swarm optimization for Doubly-Fed induction generator connected to the grid during a voltage dip. Bull Electr Eng Inform 10(5):2367–2376
Labdai S, Hemici B, Nezli L, Bounar N, Boulkroune A, Chrifi-Alaoui L (2019) 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 2019—Proceedings
Bekakra Y, Attous DB (2014) Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT. Int J Syst Assur Eng Manag 5(3):219–229
Dai H, Chen D, Zheng Z (2018) Effects of random values for particle swarm optimization algorithm. Algorithms 11(23):1–20
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Chetouani, E., Errami, Y., Obbadi, A., Sahnoun, S. (2022). Artificial Intelligence Based on Particle Swarm Optimization for Optimal Wind Turbine Power Control Using Doubly Fed Induction Generator. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_8
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