A field‑deployable calibration platform for on‑line dissolved gas monitoring systems in power transformer oil

• A field-deployable platform for calibration of on-line DGA systems is presented • Standard oil samples are prepared for multi-gas accuracy evaluation • Full-chain calibration under field conditions is demonstrated • Performance limitations of a commercial on-line DGA device are revealed • A standardized methodology for field calibration is established On‑line dissolved gas analysis (DGA) of transformer oil is essential for diagnosing incipient insulation faults and ensuring the operational reliability of power transformers. However, long‑term operation leads to sensor drift and reduced sensitivity, which eventually result in inadequate calibration and degraded diagnostic accuracy. To address these issues, this study proposes a comprehensive field‑deployable calibration platform based on precisely prepared standard oil samples. An integrated preparation‑calibration platform is developed, incorporating controlled single‑component gas injection, constant‑temperature and constant‑pressure oil‑gas equilibrium, automated multi‑level concentration switching, and pipeline self‑cleaning. The proposed platform enables accurate preparation of dissolved‑gas reference samples with high linearity (R² ≥ 0.99). A complete on-site calibration workflow is established and validated on a 220 kV hydropower transformer. Based on comparative calibration using the prepared standard oil samples, results show that the tested commercial on‑line DGA device exhibits substantial deviations, indicating systematic accuracy limitations under field operating conditions. Statistical analysis including standard deviation, coefficient of variation, and error‑distribution plots further confirms poor repeatability of the device. A qualitative post‑calibration improvement trend is also provided to illustrate the expected correction behavior. The proposed methodology provides a full‑chain evaluation framework and a practical, standardized solution for field calibration of on‑line DGA systems, forming a methodological basis for large‑scale deployment of condition-based maintenance (CBM) strategies in smart‑grid applications.