A Framework of Reduced Sensor Rooftop SPV System Using Parabolic Curve Fitting MPPT Technology for Household Consumers

Abstract

In this paper, a novel Parabolic Curve-fitting based Hill Climbing (PCHC) MPPT (Maximum Power Point Tracking) algorithm is developed to extract maximum power from solar photovoltaic (PV) panels under dynamic environmental conditions for household consumers. Moreover, this developed PCHC algorithm is integrated with a novel reduced sensor-based approach, where only a single current sensor is used to complete the solar-powered battery charging process. The ease of implementation with a simple controller and reduced sensor approach makes it economical for residential rooftop SPV. In the proposed PCHC methodology, two consecutive points on the Power-Voltage (P-V) characteristic of PV are used to detect the Maximum point region quickly. Moreover, the parabolic nature of the P-V characteristic near the Maximum Power Point (MPP) zone is explored to calculate the approx value of voltage corresponding to the optimum power. Here in every iteration, the size of the perturbation in duty is reduced by fifty percent. It overcomes the trade-off between tracking speed and oscillations near the MPP of conventional MPPT techniques such as perturb and observe, incremental conductance. These oscillations affect the connected consumers’ load working efficiency and lifetime. Moreover, it also improves the dynamic performance by taking different step sizes for sudden and slow changes in irradiation levels. The boost converter has been used to realize the performance of the proposed PCHC MPPT technique. The performance of the PCHC MPPT algorithm has been validated on different types of irradiation patterns and obtained results that fulfill the motive of the work.