The content of hydrogen is a key quantity in condition assessment and fault diagnosis of power transformer. Based on the density functional theory (DFT), the adsorption mechanism of Cu-doped SnO₂ surface toward H₂ has been systematically studied in this work. Firstly, the relaxation, the bond length, and overlap population of both the pure and Cu-doped SnO₂ are computed. To determine the optimal doping position, the formation energies of four potential sites (i.e., Sn5c, Sn6c, Sn5c-s, and Sn6c-s) are then compared with each other. The adsorption energy and the electronic structure of SnO₂ surface are analysed and discussed in detail. Furthermore, to estimate the partial atomic charges and the electrical conductance, the Mulliken population analysis is also performed. It has been found that the bridge oxygen is the most favourable position. The partial density of states of H₂ after adsorption is broadened and shifted close to the Fermi level. A large amount of charges would be transferred and then released back into its conduction band, leading to the reduction of resistance and the enhancement of sensitivity toward H₂. The results of this work provide references for SnO₂-based sensor design.

Dissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM–ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM–ANFIS.

In this paper, an online monitoring system based on a SnO ₂ gas chromatographic detector for power transformer condition assessment is developed. For a quantitative analysis of feature gases dissolved in transformer oil, a mathematical model derived from the chemistry and semiconductor theory is proposed. On the basis of this, gas chromatography measurement module along with electronic controlling and data sampling system is designed and integrated. A series of repeatability test and quantitative analysis has been performed; the repeatability performance is excellent for given concentrations; the measurement accuracy based on the proposed mathematical model for the feature gases shows good suitability. Furthermore, the transformer diagnosis is performed for identifying fault types. The experimental and practical application results demonstrate the effectiveness of this system.

Dissolved gas analysis (DGA) is widely used in diagnosing the incipient faults of power transformer. The detector is the key component for this method. Despite various sensors for DGA application, many of them suffer from poor stability and low sensitivity. A solid oxide fuel cell (SOFC) detector is designed in this paper to address these problems. The mechanism of the detector is briefly introduced, and it is fabricated in a mold. The test process is elucidated. For calibration purpose, two mathematical models of the detector, i.e., double-logarithmic model (DLM) and linearity model (LM), are compared with each other. Experimental results demonstrate that the SOFC detector exhibits good repeatability at given concentrations and excellent linearity with DLM. The results indicate that accurate and high-precision measurement of H2, CH4, CO, C2H4, C2H6, and C2H2 can be achieved using the SOFC detector and the proposed DLM.