Abstract
The output power of PV array changes with the variation of environmental factors, such as temperature and solar irradiation. Therefore, a maximum power point (MPP) tracking (MPPT) algorithm is essential for the photovoltaic generation system. However, theP-Ucurve changes dynamically with the variation of the environmental factors; here, the misjudgment may occur if a simple perturb-and-observe (P&O) MPPT algorithm is used. In order to solve this problem, this paper takes MPPT as the main research object, and an improved MPPT algorithm for PV generation applications based onP-Ucurve reconstitution is proposed. Firstly, the mathematical model of PV array is presented, and then the output dynamic characteristics are analyzed. Based on this, aP-Ucurve reconstitution strategy is introduced, and the improved MPPT algorithm is proposed. At last, simulation and comparative analysis are conducted. Results show that, with the proposed algorithm, MPP is tracked accurately, and the misjudgment problem is solved effectively.
Highlights
It is universally acknowledged that photovoltaic power generation as a new green technology has become one of the hot spots at home and abroad
The commonly used algorithms include the method based on optimization mathematical model, the method based on the output control, the method based on intelligent control and nonlinear control, and the method based on perturbation of optimization [2]
It can be seen from the output characteristic analysis of PV cell mentioned above that the output characteristic curves of PV array change with external environmental factors, such as solar irradiation and temperature
Summary
It is universally acknowledged that photovoltaic power generation as a new green technology has become one of the hot spots at home and abroad. Incremental conductance algorithm is an improved perturb-and-observe algorithm [9,10,11,12,13,14], with which the MPP is tracked by incremental conductance and instantaneous conductance of PV array. In this method, the physical concept is clear. Many scholars study the MPPT algorithm based on intelligent control and nonlinear control such as fuzzy algorithm [15], siding algorithm [16], Particle Swarm Optimization (PSO) [17], and genetic algorithm [18] These algorithms do not need to establish accurate mathematical model of the controlled object, their control
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