*2.2.3 Human-based*

Harmony Search (HS) [30], Bacterial Foraging Algorithm (BFA) [31], Simplified Teaching-Learning-Based Optimization (STLBO) [32], Discrete Symbiosis Organism Search (DSOS) [33], Artificial Immune system (AIS) [34].

## *2.2.4 Swarm-based*

The swarm-based, Particle Swarm Optimization (PSO) [3, 35, 36], Bird Mating Optimization (BMO) [37], Artificial Bee Swarm Optimization (ABSO) [38]. Grey Wolf Optimizer (GWO) [39], Chaotic Whale Optimization Algorithm (CWOA) [40], Cat Swarm Optimization (CSO) [41], and Cluster Analysis (CA) [3].

The metaheuristics are more attractive than the deterministic traditional methods in terms of accuracy and robustness, by the cause of their good global research achieving. Besides, they do not require a gradient or differentiable of the objective function. Besides, the initial guess of parameters values is not a necessity, but it necessitates the upper and lower limits of an interval of research.

## **2.3 Hybrids**

The hybrid method combines different approaches. These methods make a mix of other methods, i.e. analytical and numeric-traditional methods [15]; analytical and meta-heuristics, numeric-traditional and meta-heuristics optimization; a combination of two different meta-heuristics, etc. [38]. We can site, hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy (EHA-NMS) [41], Nelder-Mead simplex algorithm based on eagle strategy (EHA-NMS) [41], Nelder-Mead and Modified

**35**

**Figure 3.**

*PV cell's electrical equivalent circuit (SDM) [12].*

*Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells*

Particle Swarm Optimization (NM-MPSO) [42], Artificial Bee Colony-Differential Evolution (ABC-DE) [43], Trust-Region Reflective deterministic algorithm with the Artificial Bee Colony (ABC-TRR) [43], Teaching–learning–based Artificial Bee Colony (TLABC) [43]. Our proposed Levenberg-Marquardt with Grey Wolf optimizer (LM-GWO), and so on. Those methods, which are called hybrid, have excellent performances because they restrict the universe in the search process without losing precision (without losing the optimum). They achieve outstanding results with a smaller number of iterative steps when compared with pure optimization methods.

There are several electrical models, used by researchers, to describe the physical behaviors of PV cells. The Single Diode Model, containing the five unknown parameters, used in this paper is represented in **Figure 3**. By the cause of compromise

The mathematical expressions related to the current-voltage, (I-V) relationship

 <sup>+</sup> <sup>+</sup> =− −− .

*s t VRI*

*V RI II I e*

The overhead mathematical equation is in a nonlinear form and has a set of five unknown parameters (*I*L*, I*ds*, n, R*s*, R*sh). The main challenge is to get the accurate values of all the PV model's parameters values while keeping a reasonable computa-

Several approaches permit the formulation of the optimal nonlinear PV parameters determination problem, using the error (between real and simulated data) [10]. Our focus is to estimate the PV parameters values of the SDM model using RTC

about 300°C. We do not review the identification process as detailed on our previous work [20]; our focus is restricted on the third part of identification process, which is the estimation of PV parameters values. The big focus is to optimize the damping factor of LM through GWO. The characteristics of RTC France Silicon-cell

The real experimental data used of RTC France are presented on the following

. . <sup>1</sup>

*n V s*

=−− *L D sh II I I* (1)

*<sup>R</sup>* (2)

and of temperature

*sh*

*DOI: http://dx.doi.org/10.5772/intechopen.93324*

**3. Modeling and problem formulation**

of the PV cell is as follow.

tional effort.

**Table 2**.

between accuracy and simplicity, the SDM is selected herein.

*L ds*

France data at the conditions of irradiance about 1000 W/m2

data from datasheet are presented on the following **Table 1**.

*Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells DOI: http://dx.doi.org/10.5772/intechopen.93324*

Particle Swarm Optimization (NM-MPSO) [42], Artificial Bee Colony-Differential Evolution (ABC-DE) [43], Trust-Region Reflective deterministic algorithm with the Artificial Bee Colony (ABC-TRR) [43], Teaching–learning–based Artificial Bee Colony (TLABC) [43]. Our proposed Levenberg-Marquardt with Grey Wolf optimizer (LM-GWO), and so on. Those methods, which are called hybrid, have excellent performances because they restrict the universe in the search process without losing precision (without losing the optimum). They achieve outstanding results with a smaller number of iterative steps when compared with pure optimization methods.
