Hydrogen Gas Detection System in Transformer Oil Based on Big Data Analysis

The current hydrogen detection systems typically trigger warnings only after hydrogen concentration exceeds standard levels. These systems lack predictive capabilities and fail to facilitate timely maintenance, increasing the risk of transformer failures. This paper presents a novel approach, combining big data and wavelet analysis, to develop an efficient hydrogen detection system for transformer oil. The study first introduced the principle of detecting dissolved gases in transformer oil, explained the importance of hydrogen as a fault indicator, and conducted a detailed analysis of the relationship between hydrogen concentration changes and insulation aging through experiments. Experimental data shows that the hydrogen concentration increased from 15 ppm in January 2023 to 35 ppm in June 2023, reflecting the aging and fault conditions inside the transformer. The research results indicate that online monitoring of hydrogen concentration can effectively identify the type and severity of transformer faults, which has important practical application value and helps maintenance personnel take timely measures to extend the service life of transformers.