Performance Improvement of Grid Integrated SolarPV System using DNLMS Control Algorithm PROJECT TITLE :Performance Improvement of Grid Integrated SolarPV System using DNLMS Control AlgorithmABSTRACT:An integration of renewable sources based distributed generating systems encounters various power quality issues because of unsure masses at the distribution end. These uncertainties arise because of nonlinearity, disturbances or unbalanced hundreds. A three-phase grid-integrated solar photovoltaic (PV) system incorporating a control technique based on a changed decorrelation normalized least mean sq. (DNLMS) algorithm, aiming to boost its overall performance under adverse conditions, is presented during this work. The 3-part, grid-tied, single-stage solar PV system contains a solar PV array with a appropriate most power point tracking method, filters, masses, and a capacitor fed voltage supply converter (VSC). The key objective of the solar PV integrated structure with an adaptive law based management algorithm is to achieve a unity power issue (UPF) at the grid end ensuring harmonics mitigation from the grid currents. Moreover, this structure effectively transfers active power from the PV array to the native loads and therefore the grid. These aforesaid objectives are achieved through providing controlled switching pulses to the insulated gate bipolar transistor primarily based VSC using the modified DNLMS control algorithm with fast convergence rate. Harmonics-free, sinusoidal reference grid currents, are obtained by using the modified DNLMS algorithm. A simulation model developed in MATLAB/Simulink is used for the validation of the changed DNLMS-based mostly control approach. Within the laboratory, an experimental prototype is developed and the proposed algorithm is implemented to verify its performance. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest High Voltage Gain Interleaved Boost Converter with Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications