Design analysis of intelligent controller to minimize harmonic distortion and power loss of wind energy conversion system (grid connected)

Abstract

The controlling of internal parametric variations in addition to the non-linearity of a large conversion system of wind energy (WECS) is prime challenges to make the most of the generated energy, with less power loss and secure the proficiency (η) integration conventional grid. An adjustable speed control structure of grid-connected conversion system of wind energy (WECS), with the help of a Permanent Magnet Type Synchronous Generator with intelligent controller minimizes the power loss. The control system incorporates a pair of controllers dedicated to the converters of both the generator and grid edge. The controller at the generator side has the main function is to optimize power that can be withdrawal from the wind by intelligently regulating the turbine’s rotational speed. Meanwhile, the grid edge converter effectively manages active and reactive power by manipulating the d & q-axis current components, respectively. This paper discusses about the improvement in performance of the system when using Neuro-Fuzzy system as compared to Neural Network and Management of energy deliver system via direct control method. The findings reveal that the training time for Artificial Neural Networks (ANNs) is substantial, leading to the Neural Network-Direct Power Contol (NN-DPC) approach being the slowest option among the alternatives. Additionally, the NF-DPC system is less time-consuming than the NN-DPC, with a recorded duration of 24 seconds compared to the NN-DPC’s observation of 8 min and 5 s. However, it is worth noting that the NF-DPC system is somewhat more time-intensive than Common-Direct Power Contol (C-DPC).