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

  • Moulay Rachid DouiriEmail author
Part of the Power Systems book series (POWSYS)


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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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Higher School of TechnologyCadi Ayyad UniversityEssaouiraMorocco

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