Parametric estimation in photovoltaic modules using the crow search algorithm

Abstract

<p>The problem of parametric estimation in photovoltaic (PV) modules considering manufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R<sub>p</sub> and R<sub>s</sub>)), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 × 10<sup>−28</sup> and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.</p>

Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

“…To deal with the problem of parametric estimation in single-phase transformers modeled in the previous section, this research proposes the application of the recently developed metaheuristic optimization algorithm known as the crow search algorithm [22,26]. The main idea of the CSA is to model the way crows search (steal) for their food and store it in secret places [27,28].…”

“…In this research, the crow search algorithm is selected as the combinatorial optimization methodology to solve the studied problem based on the following facts: (i) The CSA is a metaheuristic optimization method from the family of bio-inspired algorithms that presents a balanced performance between the exploration and exploitation of the solution space, using memories to maintain and present the most promissory solutions of the solution space, as well as generating new solutions that allow to explore nonvisited solution regions; (ii) the effectiveness and robustness of the CSA to solve nonlinear nonconvex optimization problems has recently been demonstrated in similar optimization problems such as parametric estimation in photovoltaic modules [22], parametric estimation in induction motors [23], optimal phase-swapping in electrically unbalanced distribution networks [24], and segmentation of magnetic resonance images [25], among others; (iii) In the current literature, there is no evidence of the application of the CSA to the problem addressed in this research, which is an opportunity of research to which this paper tries to contribute.…”

“…The position of the crow i, i.e., x(i, t), can have two possible states as a function of the possibility that the crow j knows that it is being followed. This situation is governed by the awareness probability factor, i.e., A p [22].…”

“…The CSA, similar to any combinatorial optimization technique, explores and exploits the solution space by using a modification of the objective function named as the fitness function [22]. This function allows including all the constraints using penalty factors.…”

Analysis of Electrical Models for Photovoltaic Cells under Uniform and Partial Shading Conditions

“…For instance, the Slime Mold Algorithm (SMA) [9], the Grasshopper Optimization Algorithm (GOA) [10], Principal Component Analysis (PCA) [11], Particle Swarm Optimization (PSO) [12], Triple-Phase Teaching-Learning-Based Optimization (TPTLBO) [13], and Perturbed Stochastic Fractal Search (pSFS) [14] have been used to extract the parameters of the SDM. For DDM, some of the solutions that have been adopted include the moth flame optimization [15], improved differential evolutionary algorithm [16], the Pattern Search (PS) algorithm [5], the Crow Search Algorithm [17], and the Wind-Driven Optimization (WDO) algorithm [18]. However, Genetic Algorithms (GA) are the most widely adopted solution for the parameter estimation in PV systems.…”