Design and investigation of high power quality PV fed DC-DC boost converter

Abstract

This study presents a new improved voltage gain dc-dc converter architecture to maximize solar photovoltaic (PV) power output. The maximum power point tracking (MPPT) method utilizes particle swarm optimization (PSO)–based artificial neural networks (ANN) to reduce the oscillations of output electrical performance at the maximum power point (MPP). For any solar cell temperature and irradiance, the ANN delivers the electrical current and voltage outputs at the MPP and thus could reach the MPP in minimum instances. A DC-DC converter needs specific characteristics to work with photovoltaic systems. These include higher voltage development to meet increased DC link voltage requirements, the continuous input current to extend the PV system life, common grounding to prevent electromagnetic interference, and reduced electrical stress with fewer components. This paper combines a quasi-switched switch inductor network and extendable diode capacitor modules to achieve the quality mentioned earlier. The converter offers constant input current, high efficiency (95 %) with considerable gain (12 times higher than input), lower voltage stress (almost one-fifth of output voltage), and a common grounding feature. The effectiveness of the proposed MPPT technique is analyzed in terms of higher efficiency, MPP tracking time, gain, and the normalized voltage stress on diodes. The experimental step is developed to validate the simulation (Simulink and Psim) results of the present research work.