Abstract
To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.
Highlights
The parameter settings and dataset descriptions are described in detail to illustrate all the dimensions of our experiments which are performed on an Intel(R) Core (TM) i7-4700MQ CPU @ 2.40 GHz device with 32 GB of RAM, using MATLAB R2019a to implement the algorithms
The second most important parameter with a major effect on the performance of the proposed algorithm is the population size: a large population size will increase the diversity among the members, reducing the possibility of reaching the optimal solution; a small population size skips a lot of the regions within the search space, which may include the promising solution
The second strategy known as the ranking method-based-novel updating method (RUM) is integrated with generalized normal distribution optimization (GNDO) to replace the unbeneficial individuals with others created based on a novel updating method so that most regions within the search space are explored as much as possible
Summary
Due to the significant financial and environmental issues with conventional energy sources, such as fossil fuels, there has been considerable interest in clean, renewable energy sources (RESs) [1,2]. Among the RESs, solar energy—using photovoltaic (PV) systems to convert solar energy into electricity—is the second most used RES worldwide, after wind energy [3]. Since PV systems rely on solar energy, their performance is significantly influenced by variations in solar irradiance levels and in temperature. To optimize the performance of these systems before installation, suitable mathematical models are required to accurately simulate the behavior of the PV system under different operation conditions [4,5,6]. The three most common PV system models found in the literature are the single-diode model (SDM) [7,8], the double-diode model (DDM) [7,8], and the triple-diode model (TDM) [9,10]
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