An enhanced Monte Carlo outlier detection method

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology

Electrical insulators used in transmission lines and outdoor substations are exposed to severe environmental pollution, which significantly increases the risk of power system failure, especially when the pollution layer is highly humid due to adverse weather conditions. The focus of this paper is to establish an effective method for assessing the moisture content (MC) in pollution layers as it serves as a crucial indicator for evaluating the risk of failure in insulators. Hyperspectral imaging (HSI) technology with a spectral range of 371.08-1037.89 nm was applied to determine significant changes in reflectance spectral characteristics in insulators during dynamic wetting and drying periods. Partial least squares regression (PLSR) models were utilized to evaluate the data presentation enhancement abilities of spectral transformation models and the data dimensionality reduction abilities of characteristic band selection methods. Furthermore, PLSR models were developed to calculate the MC along the pixel dimension to visually retrieve the dynamic wetting and drying processes of the pollution layer. The R-squared and root-mean-square error (RMSE) results in the cross-verification set and prediction set of the RE-RF(70%)-PLSR model with two characteristic bands with a wavelength of 543.28 nm and 848.01 nm were as follows: RCV2 = 0.9824, RMSECV = 0.0367, RP2 = 0.9818, RMSEP = 0.0369, respectively. This research contributes towards the visualization retrieval of the MC and offers an important technique for analyzing flashover evolution, optimizing insulator design, and preparing coating materials for insulators.

A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine

Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range. To determine a feasible method with a higher sensitivity and broader application range, the active ingredient of deltamethrin formulation was comprehensively investigated by combining the spectral data fusion strategy with ELM by employing UV-vis, NIR and fusion strategies, individually. Consequently, the results demonstrated that the low-level fusion strategy exhibited better predictive ability (lower RMSEP of 0.0645% and higher R² of 0.9978) than mid-level fusion and individual methods. ELM combined with data fusion is proved to be an efficient method for the rapid analysis of deltamethrin formulations. Furthermore, this study provides a potential approach for pesticide quality control as well as on-site monitoring.