Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

“…For example, SaDE 42 combined two mutation operators including DE/rand/1 and DE/current-to-best/1. Liang et al 43 introduced a SEDE which divided three different mutation operators (i.e., rand/1, current-to-rand/1, and current-to-best/1) into two overlapping groups including the exploration group (rand/1 and current-to-rand/1) and exploitation group (current-to-best/1 and current-to-rand/1). Nguyen et al 66 introduced a modified DE with a mutation scheme by combining rand/1 and best/1 operators.…”

“…Recently, the DE technique and its variants have been increasingly applied for multidisciplines such as parametric estimation of the solar cell, 32 optimized the parameters of the deep belief network (DBN), 33 an improved image denoising technique, 34 the composite structure, 35 fuzzy inference-based DE, 36 deep neural network-based DE. 37,38 The performance of the DE algorithm and its variants depend on various factors such as the relation between parameters and algorithmic behavior as done in Caraffini et al, 39 the effect of crossover on the behavior of DE as Zaharie, 40 the effect of mutation scheme as JADE in Reference [41], self-adaptive differential evolution (SaDE) in Reference [42], a self-adaptive ensemble-based differential evolution (SEDE), 43 analysis of critical values for the scaling factor and crossover rate as Daniela Zaharie, 44 analysis the population size parameters, 32,45 improved the efficiency of DE through the population midpoint analysis as Arabas, 46 discussed theoretical results on the diversity and dynamics of the population in DE. 47 However, according to the "no free lunch for optimization" theorem in Reference [48], to obtain good performances, general-purpose algorithms need to be finetuned and modified with problem-specific operators.…”

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

“…Few studies employed Lambert W function [87]- [89], NR [61], [90], f-solve [91], Taylor series [92], Levenberg Marquardt [17], Bezier Curve [93], and least square nonlinear curve fitting method (lsqcurvefit function) [94]. On the other hand, the nonlinear and multi-variable PV model equation is generally solved linearly [10], [29], [34], [37], [50], [83], [95], [96], showing a theoretical gap in this field. The downsides of these methods are that some of them need a considerable execution time and cannot properly imitate the experimental current especially when the number of the experimental data contains a large number of data points, hard edges, and noises.…”

Fotovoltaik Modellerin Parametre Çıkarımı İçin Geliştirilmiş Bir Kaotik Tabanlı Balina Optimizasyon Algoritması

“…tarafından geliştirilmiştir. Tekli, çift ve modül olmak üzere üç farklı FV modelin parametrelerini SEDE algoritması ile elde etmişlerdir [12]. Li ve ark.…”