Frequency modulation analysis of solar array using genetic algorithm

Abstract

In this paper, the optimal placement of prestress (OPP) is investigated for solar array frequency modulation using the genetic algorithm (GA). The purpose of OPP is to improve the solar array’s fundamental frequency and prevent coupling resonance between the solar array and the microwave imager. Prestress is applied to a solar panel by the tension actuators. For optimization producers, the finite element model is used to analyze the prestress configuration problem, which can be converted into the number of programming decision constraints. The automatic interactive updating calculation between MATLAB and finite element software is realized by programming. The GA is used to optimize the solution. Region detection is used, and the bad individuals can be eliminated directly to avoid fitness calculation and effectively improve the optimization efficiency. Simulations are conducted for a solar panel with four arrays. Single-, two-, and three-plate optimizations are investigated. Four optimization parameters include the abscissa value, ordinate value, the laying direction of the actuator in the global coordinate system, and the prestress magnitude. Results demonstrate that the fundamental frequency reaches the maximum at a horizontal layout of prestress after the frequency modulation. The optimal solution is obtained when the prestress is placed in No.2, No.3, and No.4 plates.

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

“…The proposed feature selection method not only reduces the number of model variables but also decreases the model s complexity, thereby improving the predictive performance and robustness of the model. Based on spectral preprocessing, we utilized five conventional methods, namely genetic algorithm (GA), successive projections algorithm (SPA), UVE, CARS, and least angle regression (LARS) [28][29][30][31][32], for variable selection of the spectral data. The goal was to select appropriate spectral variables to be used in the quantitative analysis of P. massoniana seedling moisture content.…”