Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system - Tiwari, Ramji; Krishnamurthy, Kumar; Neelakandan, Ramesh; Padmanaban, Sanjeevikumar; Wheeler, Patrick (MDPI, 2018)
This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.