Noisy mixed multi-source partial discharge (PD) signals pose a significant threat to the safe operation of high-voltage direct current (HVDC) gas-insulated lines (GILs). Most existing methods focus on denoising single-type PD signals and specific types of interference, often relying on prior assumptions about noise characteristics or the number of discharge sources. These methods struggle with realistic conditions involving multiple discharge sources, various noise types, multi-band PD spectra, and overlapping narrowband interference. To address these challenges, this paper proposes a fault diagnosis framework integrating interference suppression and signal separation. First, narrowband noise is suppressed in the frequency domain using a moving median threshold and Akima cubic interpolation, while white noise is reduced through a sign-error least mean square adaptive filter. Then, time-domain, frequency-domain, and time-frequency features are extracted from the denoised signals and standardized via Z-score normalization. The standardized features are first reduced using t-distributed stochastic neighbor embedding and then clustered using density-based spatial clustering of applications with noise to achieve signal separation. The proposed noise suppression and signal separation techniques are compared with existing methods, demonstrating superior performance. Experimental results under both controlled and near-realistic conditions confirm the proposed method’s effectiveness in suppressing narrowband and white noise and in successfully isolating multiple PD sources and external corona interference, providing a practical solution for intelligent fault diagnosis in HVDC GILs.

Tip discharge in oil-immersed transformers poses a significant threat to insulation integrity. Conventional detection methods, such as gas and electrical analysis, are limited by slow response times or susceptibility to interference. Additionally, the lack of systematic comparisons between aged and fresh oil using multi-modal signal correlations hinders the development of accurate diagnostic strategies. To address this, a multi-modal sensing platform employing optical, UHF, and HFCT sensors, complemented by visual observation, was developed to investigate the evolution characteristics and mechanisms of tip discharge and to compare the detection effectiveness of these methods. Experimental results reveal that aged oil undergoes a novel four-stage evolution, where discharge signals first rise to a local peak, then experience suppression, followed by a dramatic surge, and finally decline slightly before breakdown. This process is governed by an “Impurity-Assisted Cumulative Breakdown Mechanism,” driven by impurity bridge growth and space charge effects, with signal transitions from ‘decoupling’ to synchronization. The optical sensor demonstrated superior sensitivity in early discharge stages compared to electrical methods. In contrast, fresh oil exhibited a “High-Field-Driven Stochastic Breakdown Mechanism,” with isolated pulses from micro-bubble discharges maintaining a metastable state until a critical threshold triggers instantaneous failure. This study enhances the understanding of how oil condition alters discharge mechanisms and underscores the value of multi-modal sensing for insulation condition assessment.

Transformer partial discharge (PD) diagnosis may simultaneously face narrowband interference under undersampling conditions, limited fault samples, class imbalance, and multi-source signal mixing. To address these issues, this paper proposes a multi-modal pulse-sequence-based diagnostic framework using synchronized Optical, ultra-high-frequency (UHF), and high-frequency current transformer (HFCT) measurements, and experiments are conducted on a laboratory platform with five typical PD defect models of oil-immersed transformers. For front-end signal processing, a spectral dilation and linear trend replacement (SDLTR) method is proposed to suppress narrowband interference in HFCT signals while preserving the original pulse timing and amplitude characteristics. On this basis, conventional single-sensor pulse sequence analysis (PSA) is extended to an adaptive tri-modal PSA fusion scheme for single-source PD classification. By constructing the temporal union of synchronized Optical, UHF, and HFCT pulse streams and using expert-weighted decision fusion, the proposed method exploits cross-modal complementarity and enlarges the effective sample set. Under class-imbalanced conditions, the four-pulse-based PSA6 fusion scheme achieves an accuracy of 95.47% and a Macro- F 1 of 95.17%. For dual-source PD mixtures, an adaptive cascaded decoupling framework (ACDF) is further proposed by combining class-level precision-weighted fusion, an adaptive confidence boundary, and two-stage dominant-source stripping based on PSA6 and PSA4. The proposed framework produces zero false decisions in single-source verification and correctly identifies both PD sources in all ten dual-source combinations. These results demonstrate that the proposed framework provides an effective and practical solution for transformer PD diagnosis under complex operating conditions.

When a lightguide rod extracts partial discharge (PD) light from gas-insulated switchgear (GIS), losses introduced by the rear-section fixture and aperture mismatch with the fiber entrance limit the usable optical output. Ray tracing was used to examine how a parabolic taper affects optical transmission and reception through a finite aperture. The selected design increased the mean radiant flux received at the fiber entrance to 7.26 times the baseline value. In integrating sphere tests under uniform illumination, the sample with a parabolic taper provided a higher detector output than the other three samples under all tested conditions. In needle–plate corona tests in a laboratory GIS model, the main optical responses corresponded in time to the ultrahigh-frequency (UHF) envelope and shared the same temporal features. The optical channel also recorded clusters of low-amplitude responses during the negative half-cycle that were not clearly resolved by the UHF channel; these responses were accompanied by activity in the auxiliary ultrasonic channel. These results identify rear-section transmission loss and finite-aperture reception as constraints on lightguide design and provide experimental evidence for coupling lightguide rods to transmission fibers in optical PD detection in GIS.