A grid-tied microgrid equipped with a UPQC generates clean, efficient power. ANN-controlled voltage source converters UPQCs may be more economical and less complex. This controller replaces costly DSPs, FPGAs, and a-b-c-d-q-0 conversion. A photovoltaic-battery-unified power quality conditioner system with harmonic supply voltage and nonlinear unbalanced loads is analyzed using an artificial neural network controller. Consider sag and swell voltage. The Levenberg-Marquardt algorithm is used for backpropagation in artificial neural network control. This control method sustains the target voltage of the dc-link capacitor using dependable reference signals. MATLAB/Simulink simulates photovoltaic-battery-unified power quality conditioner systems using synchronous reference frame and artificial neural networks. The testing indicated that the proposed technique was the most effective. The impacts of supply currents and dc-link capacitor voltage in relation to synchronous reference frame (SRF) and artificial neural network (ANN) controller-based universal power quality conditioner (UPQC) are also analyzed. The proposed controller surpasses the total harmonic distortion of load voltage and supply current. In contrast to SRF-based and ANN control.
Maximum Power Point Tracking; Artificial Neural Network (ANN); UPQC; Synchronous Reference Frame; Total Harmonic Distortion (THD).
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