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The rapid development of power systems has raised the requirements of the online monitoring of cable insulation performance. Considering that a signal extracted using traditional online monitoring technology is weak and vulnerable to environmental electromagnetic interference, this article proposes using the impulse impedance spectrum to monitor the insulation aging degree of a cable online. First, the amplitude–frequency characteristics of the impulse voltage are calculated, and the feasibility of using the impulse voltage for online monitoring is analyzed. Then, adopting the two-port circuit model of a cable, the impulse impedance spectrum of a cable is derived to characterize the insulation performance, and the process of monitoring of a cable online is proposed. Finally, the change law of the impulse impedance spectrum is analyzed in an experiment and simulation, and the feasibility of the impulse impedance spectrum for online monitoring is verified. The impulse signal is parasitic in the power system, has a wide frequency range, contains abundant information, has a large amplitude, and has a strong anti-interference ability. The utilization of the impulse voltage makes full use of the inherent information of the power system, and the proposed use of the impulse impedance spectrum is expected to promote the development of online monitoring technology and improve the operational stability of power systems.

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.