Abstract
In order to identify the parameters of photovoltaic (PV) cells and modules more accurately, reliably and efficiently, a chaotic second order oscillation JAYA algorithm (CSOOJAYA) is proposed. Firstly, both logical chaotic map and the mutation strategy are brought in enhancing the population diversity and improving exploitation. Secondly, in order to better balance exploration and exploitation, the second order oscillation factor is added, which not only improves the diversity of the population, but also has strong exploration at the beginning of the iteration and strong exploitation at the end of the iteration. In order to balance the two abilities, the self-adaptive weight is introduced into the CSOOJAYA algorithm to regulate individuals the tendency of moving toward the optimal solution and escaping from the worst solution, so as to enhance the search efficiency and exploitation. In order to validate the behavior of CSOOJAYA, it is employed to the parameter identification problem of PV models. Finally, the experimental results show that CSOOJAYA delivers excellent behavior in the aspects of convergence, reliability and accuracy.
Highlights
In order to alleviate the greenhouse effect and solve problems such as global warming, the application of renewable energy has attracted much attention [1]
In order to improve the performance of JAYA algorithm, this paper proposes a chaotic second order oscillation JAYA algorithm (CSOOJAYA), which can identify parameters more accurately and stably
The benchmark data are extracted from reference [11]. where the data for single diode model (SDM) and diode model (DDM) were obtained from a commercial RTC silicon PV cell (1000 w/m2 at 33 ◦ C, Photowatt, Bourgoin-Jallieu, France) with a diameter of 57 mm, and the data for the PV module was obtained from a Photowatt-PWP201 (1000 w/m2 at 45 ◦ C) solar module consisting of 36 polycrystalline silicon cells connected in series (Photowatt, Bourgoin-Jallieu, France)
Summary
In order to alleviate the greenhouse effect and solve problems such as global warming, the application of renewable energy has attracted much attention [1]. PV arrays tend to degrade over time under harsh environmental conditions, significantly affecting the behavior and utilization efficiency of PV systems [5]. In order to accurately control PV systems, it is significant to adopt the experimentally measured current-voltage (I-V) data to estimate the actual behavior of PV systems at work [6]. The single diode model (SDM) and the double diode model (DDM) are widely employed and can accurately reflect the nonlinear behavior of PV panels [7]. The parameter accuracy of these models is essential for modeling, control and optimization of PV panels [8]. These parameters are susceptible to various environmental factors and become unavailable [9]. It is very significant and challenging to employ an efficient and reliable methods to identify the PV parameters
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