To realize a high response and high selectivity gas sensor for the detection dissolved gases in transformer oil, in this study, the adsorption of four kinds of gases (H2, CO, C2H2, and CH4) on Pd-graphyne was investigated, and the gas sensing properties were evaluated. The energetically-favorable structure of Pd-Doped γ-graphyne was first studied, including through a comparison of different adsorption sites and a discussion of the electronic properties. Then, the adsorption of these four molecules on Pd-graphyne was explored. The adsorption structure, adsorption energy, electron transfer, electron density distribution, band structure, and density of states were calculated and analyzed. The results show that Pd prefers to be adsorbed on the middle of three C≡C bonds, and that the band gap of γ-graphyne becomes smaller after adsorption. The CO adsorption exhibits the largest adsorption energy and electron transfer, and effects an obvious change to the structure and electronic properties to Pd-graphyne. Because of the conductance decrease after adsorption of CO and the acceptable recovery time at high temperatures, Pd-graphyne is a promising gas sensing material with which to detect CO with high selectivity. This work offers theoretical support for the design of a nanomaterial-based gas sensor using a novel structure for industrial applications.

The information of dissolved gas in transformer oil can reflect the potential fault in oil immersed power transformer. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on IFA-LPboost-CART is proposed here. First, a LPboost-CART model is established. The classification and regression tree (CART) are used as the weak classifiers, and the linear programming boosting (LPboost) ensemble learning method is used to adjust the weight of each weak classifier to construct a strong classifier. Then the improved firefly algorithm (IFA) is adopted to optimize the number of CART and the maximum number of splits of CART in LPboost-CART to obtain the IFA-LPboost-CART model. The experimental results show that, compared with the existing methods, such as CART and support vector machine (SVM), the proposed IFA-LPboost-CART model has higher fault diagnosis accuracy, which can provide technical support for transformer fault diagnosis.

To realize high response and selectivity gas sensor in detecting dissolved gases in transformer oil, in this study, the adsorption of four kinds of gases (H2, CO, C2H2 and CH4) on Pd-graphyne as well as the gas sensing properties evaluation were investigated. The energetically favorable structure of Pd doped γ-Graphyne was first studied, including the comparison of different adsorption sites and discussion of electronic properties. Then, the adsorption of these four molecules on Pd-graphyne was explored. The adsorption structure, adsorption energy, electron transfer and electron distribution, the band structure and density of states were calculated and analyzed. The results show that the Pd atom prefers to be adsorbed on the middle of three C≡C bonds and the band gap is smaller. The CO adsorption exhibits the largest adsorption energy and electron transfer and brings obvious change to the structure and electron properties to Pd-graphyne. Because of the conductance decrease after adsorption CO and acceptable recovery time at high temperature, the Pd-graphyne can be promising gas sensing materials to detect CO with high selectivity. This work offers theoretical support for the design of nanomaterials based gas sensor using novel structure for industrial application.