Ablation Experiment Results for Power Transformer Condition Monitoring in East China

Experimental results from a study proposing a multi-source deep feature extraction method for power transformer status monitoring. The dataset, 5.5 KB in size, was authored by Wenbo Zhang and last updated on April 20, 2026. It contains results from tests using real data from transformers at the Dongwu Station in the east China power grid.

Use Cases

• Benchmarking transformer fault classification models based on multi-source sensor data.
• Evaluating the performance of spatial-temporal feature fusion methods for industrial monitoring.
• Training ensemble learning classifiers for small-sample fault recognition in power systems.
• Analyzing the impact of attention mechanisms on model accuracy for condition assessment.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Based on real data from operational transformers in the east China power grid.
• Results compare a proposed method against traditional and single-modal deep models.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare, authored by Wenbo Zhang.

Collection Method

Data likely collected from a multi-channel sensor platform monitoring key variables like partial discharge acoustic signals, voltage, current, and oil-dissolved gas concentration.

Time Range

null

Freshness

Last updated 2026-04-20 17:41:38; freshness should be verified.

Geography

East China power grid, specifically Dongwu Station.