Boris Dumnic, Bane Popadic, Dragan Milicevic, Vladimir Katic, Djura Oros

Artificial Intelligence Based Vector Control of Induction Generator Without Speed Sensor for Use in Wind Energy Conversion System

Indirect field oriented control (IFOC) of squirrel-cage induction generator (SCIG) with full capacity power converter used in wind energy conversion system (WECS) is presented in this paper. In order to improve WECS reliability robust IFOC algorithm using artificial intelligence (AI) for speed estimation was developed. Estimated speed is used for realization of maximum power tracking algorithm (MPPT). Practical testing and validation of considered estimation techniques is performed using advance laboratory prototype of WECS. Extensive experimentation is conducted in order to verify efficiency and reliability of proposed speed-sensorless control technique under realistic WECS operating conditions.

Keywords:

wind energy; induction generator; rotor speed estimation; MRAS algorithm; artificial intelligence,

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IM using MRAS observer with ANN for given set of parameters. Proposed technique obviously leads the estimated signal to a finite steady state response, but the noise and oscillations make this signal unacceptable. As proposed by the theoretical discussion, to lessen the adverse effect learning coefficient should be η << 1. By manual tuning of the parameter, optimal learning coefficient is found to be η = 0.00009, while keeping the momentum term at the same value. Increment of the adjustable weight coefficient
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