Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work, an Artificial Neural Network (ANN) based technique is suggested for classifying the faults which occur in hybrid power distribution systems. Power, which is generated by the solar and wind energy-based hybrid system, is given to the grid at the Point of Common Coupling (PCC). A boost converter along with perturb and observe (P&O) algorithm is utilized in this system to obtain a constant link voltage. In contrast, the link voltage of the wind energy conversion system (WECS) is retained with the assistance of a Proportional Integral (PI) controller. The grid synchronization is tainted with the assistance of the d-q theory. For the analysis of faults like islanding, line-ground, and line-line fault, the ANN is utilized. The voltage signal is observed at the PCC, and the Discrete Wavelet Transform (DWT) is employed to obtain different features. Based on the collected features, the ANN classifies the faults in an efficient manner. The simulation is done in MATLAB and the results are also validated through the hardware implementation. Detailed fault analysis is carried out and the results are compared with the existing techniques. Finally, the Total harmonic distortion (THD) is lessened by 4.3% by using the proposed methodology.