Power Transformer Condition Assessment Results for Five States

Assessment results for power transformer operating states from a multi-source monitoring system. The dataset is 5.5 KB in size, authored by Wenbo Zhang and last updated on April 20, 2026. It likely contains classifications from experiments using real data from transformers at the Dongwu Station in the east China power grid.

Use Cases

• Training fault classification models based on multi-sensor data fusion described in the paper.
• Benchmarking deep learning methods for temporal and spatial feature extraction from sensor data.
• Studying the effectiveness of ensemble learning for enhancing model stability in industrial monitoring.

Strengths

• Data is derived from real-world experiments on power grid transformers, suggesting practical relevance.
• The associated research paper describes a sophisticated multi-channel model combining 2D-CNN and BiLSTM with attention mechanisms.
• License is CC-BY-4.0, permitting open sharing and adaptation.

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

Wenbo Zhang, via figshare.

Collection Method

Collected from a multi-channel sensor platform on transformers, as described in the associated research paper.

Time Range

null

Freshness

Last updated 2026-04-20 17:41:39; freshness should be verified.

Geography

Likely from the east China power grid, specifically Dongwu Station.