Power Quality Enhancement of Solar PV Interfaced Distribution System by Delta-Bar-Delta Neural Network

Abstract

This project presents incremental conductance method for maximum power point tracker (MPPT). Main reason to develop solar photovoltaic energy source is to reduce deterioration in power. Power quality cannot be increases by the week distribution of grid. To increases the power of the grid solar energy conversion system paramount by implementation of a robust control technique. In this project we are we have control algorithm such as delta bar delta but neural network which is in control technique by this active power will be fed to the loads and remaining power to the grid as a function of distribution static compensator it has the capability of mitigating harmonics and balancing of loss and improving power factor. The delta bar delta neural network control algorithm has a capability to adjust weight adaptively in an independent manner and it offers alleviation in the model complexity predominant during an abnormal great conditions along with a reduction in complexion time. This model is efficient utilization accomplished with incremental conductance based on maximum power point tracking techniques for validating the behaviour of proposed system and we can expect this results in the simulation via MATLAB. The realization of MPPT controller can be based on different methods and algorithms. The results and technique include incremental conductance techniques due to the reduced oscillations while determining the Maximum PowerPoint is preferred here and it is also suitable for commercial purpose.