Frequency-Based Density Estimation and Identification of Partial Discharges Signal in High-Voltage Generators via Gaussian Mixture Models

Online monitoring of partial discharge (PD) is a complex task traditionally requiring specialized expertise. However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. The proposed method employs a findpeaks algorithm based on Fast Fourier Transform (FFT) to extract frequency key features, denoted as f1 and f2, from the frequency spectrum. These features are used to estimate model parameters for each PD type, enabling the representation of their frequency density distributions in a 2D map (f1, f2) via Gaussian Mixture Models (GMMs). The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. For PD identification, log-likelihood and softmax functions are applied, achieving an evaluation accuracy of 96.68%. The model also demonstrates robust performance in identifying unknown PD data, with accuracy ranging from 78.10% to 95.11%. This approach enhances the distinction between PD types based on their frequency characteristics, providing a reliable tool for PD signal analysis and identification.