Abstract
Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results.
Highlights
In recent years, photovoltaic (PV) systems integrated into power grids have been gaining popularity as one of the most promising and reliable energies among existing renewable energy sources
Longer computation time for large search space, under various partial shading is inevitable. Another issue regarding the Particle Swarm Optimization (PSO) technique is that it can be trapped at the local peak in a high-dimensional space and has a low convergence rate [28]– [30]. To overcome these main drawbacks, this paper proposes an improved maximum power point tracking (MPPT) method based on a synergism of PSO and Differential Evaluation (DE), called the PSO-DV algorithm
This paper is a continuation of usage of computational algorithms to find the global peak (GP) of PV systems under partial shading conditions (PSCs)
Summary
Photovoltaic (PV) systems integrated into power grids have been gaining popularity as one of the most promising and reliable energies among existing renewable energy sources. In [24] similar to HC, the improved PSO works with direct duty cycle with a faster tracking speed and low oscillations at the MPP under PSCs. The method presented in [25] is a system-independent MPPT algorithm which sorts the obtained particle positions to avoid a large voltage stress on the power switch due to the sudden change of duty cycle. Another issue regarding the PSO technique is that it can be trapped at the local peak in a high-dimensional space and has a low convergence rate [28]– [30] To overcome these main drawbacks, this paper proposes an improved MPPT method based on a synergism of PSO and DE, called the PSO-DV algorithm. As these techniques are based on search optimization, the GP could be tracked with a reasonable convergence time and a better dynamic response than the conventional methods
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