# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method  
[PERSON]  
Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China  
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[PERSON]  
Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China  
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Footnote 2: email: [EMAIL_ADDRESS]. detection sensitivity, albeit susceptible to interference from environmental noise. Infrared absorption spectrometry permits simultaneous detection of multiple gases, but encounters challenges associated with the mutual interference of absorption peaks from each gas and high equipment costs [10].Within the wavelength range of 290 nm to 310 nm, SO\({}_{2}\) does not exhibit any characteristic absorption overlap with other SF\({}_{6}\) derivatives [11; 12]. Consequently, UV absorption spectroscopy presents notable advantages in SO\({}_{2}\) detection. These advantages encompass shorter detection periods, reduced equipment costs, and minimized gas consumption. In the on-site GIS insulation monitoring process, the utilization of portable ultraviolet spectrometers can be hindered by environmental noise and equipment vibrations, potentially submerging the genuine characteristics of the ultraviolet spectral signal within the noise. To facilitate a more accurate quantitative analysis of SO\({}_{2}\) within the GIS, it is imperative to undergo a denoising process of the ultraviolet spectral signal. Conventional methods for spectral data noise reduction encompass Fourier transform, wavelet transform, Savitzky-Golay filtering, and empirical mode decomposition (EMD). Fourier transform excels in analyzing signals in the frequency domain, particularly periodic ones, but falls short in extracting features from localized signals. Conversely, wavelet transform and the Savitzky-Golay filter necessitates the manual selection of various filtering parameters, rendering them less suitable for on-site inspections. Furthermore, EMD is an iterative algorithm that demands repeated signal decomposition, potentially leading to modal overlap issues and yielding unstable decomposition results. SVD, as a data-driven signal processing technique, distinguishes itself by eliminating the need for manual parameter adjustments and demonstrates superior denoising capabilities for both linear and nonlinear signals [13]. Notably, the difference between the authentic spectral signal and the noise becomes more discernible in the singular values. The crux lies in accurately identifying the boundary between them to achieve effective denoising. As a result, this study introduces a novel approach for determining the optimal order of SVD. In comparison to previous methods, this approach maximizes denoising effectiveness and optimizes computational resources to enhance both computational efficiency and denoising accuracy. The following is a summary of this paper's contribution:  
1. A passive, built-in optical sensor has been engineered, featuring a high-reflectivity concave mirror seamlessly integrated into the flange. This sensor is designed for direct mounting onto the GIS, enabling real-time online monitoring. 2. Our innovative approach involves the reconstruction of singular values from noise-inclusive signals into distinct component signals. These component signals are then integrated with the fast Fourier transform (FFT) algorithm, introducing FFT peaks as metrics to characterize the signals. These metrics are subsequently ranked through a decremental process. The singular value corresponding to the first FFT peak surpassing a predefined threshold is chosen as the effective order for denoising. 3. Detection experiments involving various concentration gradients were carried out using the SO\({}_{2}\) ultraviolet spectroscopy detection platform. The denoising performance of the proposed method was then compared to that of the conventional approach.