Abstract

Modern power systems are large and interconnected with growing trends to integrate wind energy to the power system and meet the ever rising energy demand in an economical manner. The penetration of wind energy has motivated power engineers and researchers to investigate the dynamic participation of Doubly Fed Induction Generators (DFIG) based wind turbines in Automatic Generation Control (AGC) services. However, with dynamic participation of DFIG, the AGC problem becomes more complex and under these conditions classical AGC are not suitable. Therefore, a new non-linear Least Squares Support Vector Machines (LS-SVM) based regulator for solution of AGC problem is proposed in this study. The proposed AGC regulator is trained for a wide range of operating conditions and load changes using an off-line data set generated from the robust control technique. A two-area power system connected via parallel AC/DC tie-lines with DFIG based wind turbines in each area is considered to demonstrate the effectiveness of the proposed AGC regulator and compared with results obtained using Multi-Layer Perceptron (MLP) neural networks and conventional PI regulators under various operating conditions and load changes.

Keywords:

Automatic generation control , doubly fed induction generator , RBF kernel , support vector machines,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.