Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules
The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear
optimization problem that cannot be accurately solved by traditional techniques. Therefore,
metaheuristic algorithms have been recently used to solve this problem due to their potential to
approximate the optimal solution for several complicated optimization problems. Despite that,
the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in
local optima when applied to tackle this problem. Therefore, this study presents a new parameter
estimation technique, namely HKOA, based on integrating the recently published Kepler optimization
algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to
accurately estimate the unknown parameters of the third-, single-, and double-diode models. The
former mechanism aims at promoting the KOA’s exploration operator to diminish getting stuck in local
optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge
to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and
five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-
40/36, to show their efficiency and stability. In addition, they are extensively compared to several
optimization techniques to show their effectiveness. According to the experimental findings, HKOA is
a strong alternative method for estimating the unknown parameters of PV models because it can yield
substantially different and superior findings for the third-, single-, and double-diode models.
The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear
optimization problem that cannot be accurately solved by traditional techniques.…
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