AC power

Electrical engineering

Total harmonic distortion

Power factor

Integrator

Inverter

Engineering

Voltage

Artificial intelligence

Harmonics

Control (management)

Electronic engineering

Computer science

Photovoltaic system

Maximum power point tracking

Control theory (sociology)

Active filter

IEEE Systems Journal

This article investigates the power quality enhancement of a grid-tied photovoltaic (PV) distribution system by employing a fuzzy logic proportional-integrator-derivative multiple complex coefficient filter multiple second-order generalized integrator frequency-locked loop (FLPID-MCCF-MSOGI-FLL) hybrid control scheme based shunt active power filter. The MSOGI-FLL reference current generation strategy is implemented to mitigate the current harmonics by extracting the fundamental constituents (FCs) from the nonlinear load currents, whereas an MCCF is employed to separate the FC from the distorted grid voltages and eliminates the voltage harmonics during extremely polluted grid voltage condition. The main objective of using FLPID is to maintain the stable power between dc and ac sides by regulating the dc-link voltage constant under transient conditions. To track the maximum power from the PV panel under varying environmental condition, the particle swarm optimization based perturb and observe technique is used in this article. The comparative analysis is analyzed to check the effectiveness of the proposed hybrid control scheme with existing and adaptive control techniques in respect of power quality, better dc offset rejection, better FC and frequency extraction, and grid synchronization. The system with the proposed control scheme is simulated on MATLAB/Simulink and validated in a real-time field-programmable gate array platform under different test scenarios. In conclusion, the harmonic content of grid currents and voltages is found well within the IEEE-519 standard limits.

An Improved Adaptive Control Strategy in Grid-Tied PV System With Active Power Filter for Power Quality Enhancement