Abstract
Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO2 and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power-voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power-voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single-sensor-based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single-sensor-based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.