Fixed double-row photovoltaic (PV) arrays are susceptible to wind-induced damage, while their wind load characteristics remain inadequately investigated. This study employs computational fluid dynamics (CFD) simulations to systematically analyze wind load behavior under varying operational conditions, aiming to identify critical scenarios and structural vulnerabilities. First, the validity of the CFD methodology was verified through direct comparison between wind tunnel pressure measurements of an isolated PV module and corresponding numerical simulations. Subsequently, scaled PV array models were constructed to replicate practical engineering configurations, enabling a systematic evaluation of wind direction effects on mean net wind pressure coefficients and three-component force coefficients. Finally, surface wind pressure distribution patterns were examined for four representative wind angles (0°, 45°, 135°, 180°). Results demonstrate that edge-positioned modules exhibit maximum mean net wind pressure coefficients and three-component force coefficients under oblique wind angles (45° and 135°), which are identified as the most critical operational conditions. In contrast, minimal wind loads were observed at a 90° wind angle, indicating an optimal orientation for array installation. Additionally, significantly higher surface wind pressure coefficients were recorded for edge modules under oblique winds (45°/135°) compared to both interior modules and other wind angles. It was found through the study that under upwind conditions (0–90°), the lower-row components are capable of withstanding greater wind loads, whereas under downwind conditions (90–180°), an increase in the loads exerted on the upper-row components was observed.

This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R²), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R² = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment.

The i_p-i_q harmonic detection method, which is based on instantaneous reactive power theory, involves cumbersome and complex computations. In addition, the adoption of a low-pass filter (LPF) degrades the dynamic response performance of harmonic detection. To achieve accurate and fast detection of grid harmonic currents for efficient power grid compensation, this paper proposes a total current harmonic detection method using a dual second-order generalized integrator (DSOGI). This method eliminates the calculation steps of the active and reactive components of load current that are required in the conventional i_p-i_q method. More importantly, it replaces the LPF in the traditional detection scheme with a positive-sequence fundamental component extraction structure based on the DSOGI. Simulations and experimental tests are conducted on the proposed method under balanced grid conditions; the total harmonic distortion (THD) is approximately 2%, and the system stabilizes within 0.04 s. The detection speed and accuracy of the proposed method are superior to those of the traditional i_p-i_q harmonic detection method, the sinusoidal amplitude integrator (SAI)-based method, and the complex coefficient filter (CCF)-based method.