To investigate the impact of long-term voltage stress on the surface charge accumulation and discharge characteristics of insulating materials, Epoxy/Al2O3 composites were subjected to DC and AC voltage application tests for durations of seven and 30 days. By integrating surface charge measurements, isothermal surface potential decay (ISPD) analysis, and scanning electron microscopy (SEM), the regulatory mechanisms of surface charge accumulation and trap distribution parameters on discharge behavior were analyzed. The results indicate that under long-term DC voltage, continuous unipolar charge injection induces microdefects such as grooves and pores on the material surface, leading to a significant increase in deep trap density. This enhancement in surface charge binding capability results in an increase in charge density with prolonged voltage application, which subsequently intensifies electric field distortion and partial discharge (PD) activity. On the contrary, the surface charge density under AC voltage is generally lower and exhibits an increasing trend from the high-voltage electrode toward the grounded electrode. Notably, the sample stressed for seven days exhibited the lowest surface charge density yet the most intense PD activity. This phenomenon is attributed to an increase in shallow trap density, which enhances surface conductivity and carrier mobility. Conversely, after 30 days of stress, the formation of deep traps restores the charge binding capability. Furthermore, the significant accumulation of negative charges near the grounded electrode under AC conditions is identified as a critical factor potentially triggering surface discharge.
Zonglin Wang, Hui Song, Gehao Sheng et al.· IEEE Transactions on Plasma...· 0 citations
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-F1 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.
Zehao Chen, Yong Qian, Chao Pan et al.· Measurement science and tech...· 0 citations
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