A Partial Discharge Signal Separation Method Applicable for Various Sensors Based on Time–Frequency Feature Extraction of t-SNE

Currently employed features for separating partial discharge (PD) pulse signals are proposed based on signals captured by a particular sensor. If these features are used to separate PD signals captured by other sensors, the distribution of these features may overlap, making it difficult to distinguish different PD signals. To address the challenge of poor applicability of traditional features to sensors with different amplitude–frequency characteristics in separating different PD signals, this article proposes a pulse time–frequency (T–F) feature extraction method based on t-distributed stochastic neighbor embedding (t-SNE). The proposed method uses the wavelet T–F spectrum of each PD pulse and reduces the dimensionality of the T–F spectrum by the t-SNE algorithm. The obtained features effectively discriminate between T–F spectra of different discharge types. Furthermore, to ensure satisfactory separation outcomes, the article proposes an optimization method for setting the perplexity parameter of the t-SNE algorithm based on root mean square error (RMSE). Finally, the proposed feature extraction method’s effectiveness is validated using test data from different sensors and various tested objects. Additionally, this method shows better visual separation results compared to traditional T–F features and other dimensionality reduction methods.