Advanced Rapid Predictive and Modulation Model for PV System

Abstract

In the real-time case of the power management system, renewable energy improves power generation to reduce demand. Since this type of renewable energy system is not sufficient to generate power to the load with controlled energy in grid systems and other related high-power applications. The existing models refer to the static parameters to control the regulation factor of the DC converter. This leads to a delay in step response and the other related features which will affect the output power with noise and surge peaks. In this work, a novel model of the optimal controlling model was proposed to improve the switching activities of the converter circuit. In this, the DC–DC converter design needs to enhance for reducing the noise at converted DC output. The controller tunes gain the parameters according to the feedback signal dynamically to reduce the step response time. For this process, a rapid model predictive and modulation-based controller with maximum power point tracking analyzer are used to estimate operating point of the PV system and control the switching devices accordingly. This improves the high-frequency switching activities to reduce the noise level in DC output. This can also reduce the noise at DC output. The inverter was connected at the output terminal of DC–DC converter for the AC load components. The space vector pulse width modulation technique was used for driving the inverter switches to reduce the total harmonic distortion from the output waveform to the load terminal. The simulation result and the experimental result show the performance of the proposed controller comparing with traditional controlling models.