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Advanced Control and Optimization Paradigms for Wind Energy Systems pp 213–234Cite as

Neural-Based P-Q Decoupled Control for Doubly Fed Induction Generator in Wind Generation System

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Part of the book series: Power Systems ((POWSYS))

Abstract

This chapter describes an Artificial Neural Network (ANN) approach for active and reactive decoupled control based Direct Power Control (DPC) in Doubly Fed Induction Generator (DFIG) for Wind Generation System (WGS) by using the suitable voltage vectors on the rotor side. To avoid the computational complexity of DPC, we develop a neuronal approach using an individual training technique with fixed weight and supervised networks. For this, the neural system is split into 5 sub-networks namely: reactive and real power measurement sub-networks with dynamic neurons and fixed-weight; reactive calculation and reference real sub-networks with square neurons and fixed-weight; reference stator current computation sub-network with logarithm of sigmoid, tangent sigmoid neurons and supervised weight; reference rotor current computation sub-network with recurrent neurons and fixed-weight; and reference rotor voltage calculation sub-networks with dynamic neurons and fixed-weight. Under transient conditions, and for step changes of the real and the reactive power references, the DFIG is capable of tracking the references with a response time of less than 1 s. This is fast enough for changes made by the power system operator, and for tracking wind speed variations. Thus, the sensorless measurement of the position is effective in controlling P and Q.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Higher School of Technology, Cadi Ayyad University, BP. 383, Essaouira, Morocco

    Moulay Rachid Douiri

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  1. Moulay Rachid Douiri
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Correspondence to Moulay Rachid Douiri .

Editor information

Editors and Affiliations

  1. Faculty of Automation and Computers, “Politehnica” University of Timișoara, Timișoara, Romania

    Radu-Emil Precup

  2. Department of Electrical and Electronics Engineering, Sakarya University, Serdivan, Sakarya, Turkey

    Tariq Kamal

  3. School of Electrical Engineering, Chongqing University, Chongqing, China

    Syed Zulqadar Hassan

Appendix

Appendix

Doubly Fed Induction Generator Parameters:

Rated power \(= 1\) MVA

Stator/rotor turns ratio \(=\) 1:1

Stator resistance \((R_{s}) = 0.00662\) p.u.

Rotor resistance \((R_{r})= 0.01\) p.u.

Stator inductance \((L_{s})=3.185\) p.u.

Rotor inductance \((L_{r})=3.21\) p.u.

Mutual inductance \((L_{m})=3.1\) p.u.

Base impedance \((Z_{base})=10.98\,\Omega \)

Pole pairs \((p)=3\)

Frequency \((f)=50\) Hz

Filter and Grid Parameters:

Inductor \(L=0.005\) H

Resistor \(R=0.25\,\Omega \)

Capacitor \(C=4400\,{\upmu }F\)

Turbine Parameters:

Radius of the turbine \(R_{t}=13.5\) m

Gain multiplier \(G=65\)

Inertia total moment \(J=10\,\text {Kg}\,\text {m}^{2}\)

Air density \(\rho =1.22\,\text {Kg/m}^{2}\)

Coefficient of viscous friction \(f=0.0001\)

Optimal tip speed ration \(\lambda _{opt}=8.5\)

Maximal power coefficient \(C_{pmax}=0.5\)

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Douiri, M.R. (2019). Neural-Based P-Q Decoupled Control for Doubly Fed Induction Generator in Wind Generation System. In: Precup, RE., Kamal, T., Zulqadar Hassan, S. (eds) Advanced Control and Optimization Paradigms for Wind Energy Systems. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-5995-8_9

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  • DOI: https://doi.org/10.1007/978-981-13-5995-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5994-1

  • Online ISBN: 978-981-13-5995-8

  • eBook Packages: EnergyEnergy (R0)

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