Adaptive neuron detection‐based control of single‐phase SPV grid integrated system with active filtering

Abstract

This study deals with an adaptive neuron detection-based control technique for single-phase, single-stage solar photovoltaic (SPV) array grid integrated system using a voltage-source converter (VSC). The VSC also provides power quality features such as harmonics mitigation, power factor correction and perturb and observation-based maximum power point tracking for an SPV grid-interfaced system. With this approach, the VSC has the active power flow from the SPV array to the grid and it also performs the non-linear load current harmonic compensation by keeping the grid current almost sinusoidal. On the other hand, in the night mode, if the SPV power is not available, the VSC works as an active power filter for the harmonics elimination and reactive power compensation. This increases the effective utilisation of the VSC. The adaptive neuron detection technique has lesser noise and oscillation in extraction of fundamental component in comparison of conventional adaptive control algorithm. It has also satisfied an IEEE-519 standard of harmonics by improving the quality of power of SPV grid integrated system. The proposed system is modelled and simulated using MATLAB/Simulink and the response of the system under non-linear loads and varying environmental conditions are evaluated experimentally on a prototype developed in the laboratory.