Abstract
The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.
Highlights
The power–voltage (P − V ) characteristic of a photovoltaic (PV) module shows its operating point, which denotes the point at which module can output the maximum power, and it is known as the maximum power point (MPP)
The position of the MPP depends on the external environment, and it changes with the change in the environmental conditions
Considering the mentioned particle swarm optimization (PSO) drawbacks, this paper proposes an improved method to augment the maximum-power-point tracking (MPPT) method for a PV system under partial shading
Summary
The power–voltage (P − V ) characteristic of a photovoltaic (PV) module shows its operating point, which denotes the point at which module can output the maximum power, and it is known as the maximum power point (MPP). The nearest local maximum was detected by the conventional MPPT method, and in the second stage, the obtained information was used for tracking the GP by the PSO algorithm; this method was not efficient in more complex shading conditions. Another advantage of the PSO algorithm is that it treats the maximum power tracking problem as an optimization problem. It is difficult to track the maximum power point by the conventional methods
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