Performance enhancement of photovoltaic system under grid voltage distortion utilising total least‐square control scheme

Abstract

Grid-interfaced solar photovoltaic (GIPV) system requires a robust control algorithm for satisfactory operation under grid voltage disturbances. In this study, an application of Newton's learning rule-based total least-square estimation employing single-layer neural network structure is adopted to interface the photovoltaic unit with the utility grid. In addition, the weight-updating mechanism is uniquely integrated with threshold neuron for attaining speedy extraction of fundamental load current component. In particular, the proposed control algorithm has the capability to transfer maximum active power to the utility grid/AC load at unity power factor while operating as distribution static compensator to offer various ancillary services including current harmonics attenuation and reactive current suppression. This will lead to increased device utilisation factor of the overall GIPV system during night time (i.e. in the absence of solar irradiance). Moreover, the proposed control scheme is designed and developed without any complex transformations and derivative terms, which results in less computational intensiveness. The GIPV system with three-phase double-stage configuration is modelled in MATLAB/Simulink software using sim-power system tool. Finally, the adaptability of the proposed control algorithm has been verified and confirmed through 500 W laboratory prototype using low-cost Arm Cortex-M4 microcontroller under various operating conditions.