Abstract
A partial shading condition is an environmental phenomenon that causes multiple peaks in Photovoltaic (PV) characteristics. Introducing robust and reliable Maximum Power Point Tracking technique is essential in PV systems to extract the Global Maximum Power Point (GMPP) irrespective of the environmental conditions. Therefore in this manuscript, a novel optimization algorithm is implemented for MPPT. The developed technique named Chaotic Flower Pollination Algorithm (C-FPA) merges the chaos maps (Logistic, sine, and tent maps) to tune the basic algorithm parameters adaptively. The effectiveness of the introduced variants is proved using several patterns of partial shading condition. Moreover, these variants are certified for tracking the GMPP in case of dynamic and sudden variation in the irradiance conditions. Several statistical analysis is carried out to evaluate the performance of the proposed variants in comparison with the standard version of the Flower Pollination Algorithm (FPA). The significant outcome clarifies that combining the chaos maps with FPA improves the dependability and stability of the FPA and offers higher tracking efficiency with a reduction of tracking time by 50% when compared to FPA. Moreover, the proposed C-FPA provides a better dynamic response, especially with the tent chaos map.
Highlights
Increase in energy demand and the depletion of nonrenewable energy resources brings a new challenge to the power industry sector
Intensive comparison is carried between the Chaotic Flower Pollination Algorithm (C-Flower Pollination Algorithm (FPA)) variants and the standard FPA to clarify the influence of utilizing the chaos maps for the first time in the MPPT system
Novel Global Maximum Power Point (GMPP) tracking technique variants are proposed in the current manuscript to tackle several drawbacks of the traditional algorithms
Summary
Increase in energy demand and the depletion of nonrenewable energy resources brings a new challenge to the power industry sector. The bio-inspired algorithms have been widely used due to their extensive features like, solving non-linear multi-model optimization problems effectively, with faster convergence rate, quick response with a wide range of exploration in the search which guarantees to track the GMPP under any environmental conditions [10]. By recognizing the dynamic and randomization features of chaos maps, optimization algorithms maintain a perfect trade-off between exploration and exploitation process [35], [36] With this motivation, authors in this article introduced variants of chaotic optimization algorithm for the application MPP irrespective of the environmental conditions, including with various dynamic shade patterns.
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