scholarly journals Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3925
Author(s):  
Carlos Cárdenas-Bravo ◽  
Rodrigo Barraza ◽  
Antonio Sánchez-Squella ◽  
Patricio Valdivia-Lefort ◽  
Federico Castillo-Burns
Keyword(s):  
Differential Evolution ◽  
High Performance ◽  
Short Circuit ◽  
Differential Evolution Algorithm ◽  
Open Circuit ◽  
Solar Photovoltaic ◽  
Boundary Definition ◽  
Calculation Methodology ◽  
Evolution Algorithm ◽  
Optimal Set

This study proposes a calculation methodology that determines the optimal boundary parameters of the single-diode photovoltaic model. It allows the calculation of the single-diode photovoltaic model when no reference parameter boundaries are available. The differential evolution algorithm, integrated with a step-by-step boundary definition module, is used to calculate the optimal parameters of the single-diode photovoltaic model, improving the performance of the classic algorithm compared with other studies. The solution is validated by comparing the results with well-established algorithms described in the state-of-the-art, and by estimating the five important points (cardinal points) of an IV curve, namely short-circuit, maximum power, and open circuit points, using a database composed of 100 solar photovoltaic modules. The results show that an optimal set of parameter boundaries enables the differential evolution algorithm to minimize the error of the estimated cardinal points. Moreover, the proposed calculus methodology is capable of producing high-performance response photovoltaic models for different technologies and rated powers.

scholarly journals Solar Photovoltaic Cell Parameters Extraction Using Differential Evolution Algorithm

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 43
Author(s):  
Rachid Herbazi ◽  
Youssef Kharchouf ◽  
Khalid Amechnoue ◽  
Ahmed Khouya ◽  
Adil Chahboun
Keyword(s):  
Solar Cells ◽  
Differential Evolution ◽  
Short Circuit ◽  
Differential Evolution Algorithm ◽  
Short Circuit Current ◽  
Photovoltaic Cell ◽  
Open Circuit ◽  
Good Precision ◽  
Cell Parameters ◽  
Power Point

This work presents a method for extracting parameters from photovoltaic (PV) solar cells, based on the three critical points of the current-voltage (I-V) characteristic, i.e., the short-circuit current, the open circuit voltage and the maximum power point (MPP). The method is developed in the Python programming language using differential evolution (DE) and a three-point curve fitting approach. It shows a good precision with root mean square error (RMSE), for different solar cells, lower than to those cited in the literature. In addition, the method is tested based on the measurements of a solar cell in the Faculty of Science and Technology of Tangier (FSTT) laboratory, thus giving a good agreement between the measured data and those calculated (i.e., RMSE = 7.26 × 10−4) with fewer iterations for convergence.

scholarly journals Design of a high-performance in-coupling grating using differential evolution algorithm for waveguide display

2018 ◽  
Vol 26 (20) ◽  
pp. 26646 ◽  
Author(s):  
Cheng Pan ◽  
Zeyang Liu ◽  
Yajun Pang ◽  
Xianxin Zheng ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
Differential Evolution ◽  
High Performance ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm

scholarly journals Parameter Identification of PMSM Using Immune Clonal Selection Differential Evolution Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guohan Lin ◽  
Jing Zhang ◽  
Zhaohua Liu
Keyword(s):  
Differential Evolution ◽  
High Performance ◽  
Clonal Selection ◽  
Permanent Magnet Synchronous Motor ◽  
Differential Evolution Algorithm ◽  
Model Complexity ◽  
Good Convergence ◽  
Rotor Flux ◽  
Evolution Algorithm ◽  
Stator Resistance

Permanent magnet synchronous motor (PMSM) models with accurate parameters are crucial to high performance PMSM control system designs. As the estimation of PMSM parameters is very difficult due to thenonlinearmodel complexity, a novel immune clonal differential evolution algorithm (ICDEA) is proposed to identify the electrical parameters of nonsalient pole PMSM. Clonal selection and receptor editing mechanism are introduced to ICDEA to increase the diversity of the population and improve searching capability. The effectiveness of the proposed identification method is verified by both simulation and experiment. The results show that the proposed algorithm has good convergence in simultaneously estimating stator resistance,dq-axis inductances, and rotor flux linkage. In addition, the convergence speed of ICDEA is compared with other differential evolution (DE) algorithms, which verifies that the ICDEA has better performances in global searching.

Optimization of Micro-Grid Electricity Market Based on Multi Agent Modeling Approach

2018 ◽  
Vol 7 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Alireza Heidari ◽  
Mehdi Moradi ◽  
Alireza Aslani ◽  
Ahmad Hajinezhad
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
High Performance ◽  
Differential Evolution Algorithm ◽  
Particle Swarm Algorithm ◽  
Environmental Advocacy ◽  
Multi Agent ◽  
Evolution Algorithm ◽  
Micro Grids

Micro-grids are the key technologies known to solve challenges such as increased electric demand, fatigue electric installations, electrical leakage and pressures and opposition from environmental advocacy groups. The current article is presenting an improved optimization algorithm based on a differential evolution algorithm to achieve the optimal response for managing distributed energy resources in micro-grids. The simulation results show that: 1) The final cost of network management in systems based on the agent is very favorable compared to a network regardless of the agent and also are economically much more useful and effective in coordinating various energy sources. 2) The results of the proposed algorithm are much better in comparison with the results of the Fireflies optimization algorithm, a differential evolution algorithm and the particle swarm algorithm. This comparison proves the high performance of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document