Numerical Modeling of Partial Discharge Development Process

Partial discharge (PD), a type of low-temperature plasma, indicates a discharge event that does not bridge the electrodes of an electrical insulation system under high voltage stress. It is common in power equipment, such as transformers, cables, gas-insulated switchgears, and so on. The occurrence of PD could deteriorate the insulation performance of the equipment, but, meanwhile, it is often used to diagnose the insulation status. Therefore, it is very necessary to clarify the PD mechanism, and through modeling the PD process, a better understanding of the phenomenon could be attained. Although PD is essentially a gas discharge phenomenon, it possesses some distinctive features, for example, very narrow discharge channel, short time duration, and stochastic behavior, which determine the simulation method of PD different from that for the other types of plasmas. This chapter seeks to propose a simulation method that could reflect the physical processes of PD development after introducing some background knowledge about PD and analyzing the shortcomings of existent models

Application of TiO2 Nanotubes Gas Sensors in Online Monitoring of SF6 Insulated Equipment

Titanium dioxide nanotube arrays (TNTAs) are a typical three-dimensional nanomaterial. TNTA has rich chemical and physical properties and low manufacturing costs. Thus, TNTA has broad application prospects. In recent years, research has shown that because of its large specific surface area and nanosize effect, the TNTAs have an enormous potential for development compared with other nanostructure forms in fields such as light catalysis, sensor, and solar batteries. TNTAs have become the hotspot of international nanometer material research. The tiny gas sensor made from TNTA has several advantages, such as fast response, high sensitivity, and small size. Several scholars in this field have achieved significant progress. As a sensitive material, TNTA is used to test O2, NO2, H2, ethanol, and other gases. In this chapter, three SF6 decomposed gases, namely SO2, SOF2 and SO2F2, are chosen as probe gases because they are the main by-products in the decomposition of SF6 under PD. Then, the adsorption behaviors of these gases on different anatase (101) surfaces including intrinsic, Pt-doped and Au-doped, are studied using the first principles density functional theory (DFT) calculations. The simulation results can be used as supplement for gas-sensing experiments of TNTA gas sensors. This work is expected to add insights into the fundamental understanding of interactions between gases and TNTA surfaces for better sensor design

The SF6 Decomposition Mechanism: Background and Significance

Gas Insulated Switchgear (GIS) has been widely used in substations. The insulating medium used in GIS is sulfur hexafluoride (SF6) gas. However, the internal insulation defect existed in GIS would inevitably lead to partial discharge (PD), and cause the composition of SF6 to SOF2, SO2F2 and SO2 and other characteristic component gases. The decomposition phenomenon would greatly reduce the insulation performance of SF6 insulated equipment, and even paralyze the whole power supply system. In this chapter, we first discuss the objective existence, decomposition mechanism and harmness of insulation defects. Then the methods for insulation defects detection used to avoid the insulation accidents are introduced. Comparing all of the detection methods, diagnosing the insulation defect through analyzing the decomposed gases of SF6 by chemical gas sensors is the optimal method due to its advantages, such as high detection accuracy and stability, signifying the importance of developing chemical gas sensor used in SF6 insulated equipment. In conclusion, there kinds of gas sensor material, carbon nanotubes, graphene, are chosen as the gas sensing materials to build specific gas sensors for detecting each kind of SF6 decomposed gases, and then enhance the gas sensitivity and selectivity by material modification

Multiple Factors Drive Variation of Forest Root Biomass in Southwestern China

The roots linking the above-ground organs and soil are key components for estimating net primary productivity and carbon sequestration of forests. The patterns and drivers of root biomass in forest have not been examined well at the regional scale, especially for the widely distributed forest ecosystems in southwestern China. We attempted to determine the spatial patterns of root biomass (RB, Mg/ha), annual increment root biomass (AIRB, Mg/ha/year), ratio of root and above-ground (RRA), and the relative contributions of abiotic and biotic factors that drive the variation of root biomass. Forest biomass and multiple factors (climate, soil, forest types, and stand characteristics) of 318 plots in this region (790,000 km2) were analyzed in this research. The AB (the mean values for forest aboveground biomass per ha, Mg/ha), RB, AIRB, and RRA were 126 Mg/ha, 28 Mg/ha, 0.69 Mg/ha and 0.22, respectively. AB, RB, AIRB, and RRA varied across all the plots and forest types. Both RB and AIRB showed significant spatial patterns of distribution, while RRA did not show any spatial patterns of distribution. Up to 28.4% of variation in total of RB, AIRB, and RRA can be attributed to the climate, soil, and stand characteristics. The explained or contribution rates of climate, soil, and stand characteristics for variation of whole forest root biomass were 6.7%, 16.9%, and 10.9%, respectively. Path analysis in structural equation model (SEM) indicated the direct influence of stand age on RB. AIRB was greater than that of the other factors. Climate, soil and stand characteristics in different forest types could explain 9.7%–96.1%, 15.4%–96.4%, and 36.7%–99.4% of variations in RB, AIRB, and RRA, respectively, which suggests that the multiple factors may be important in explaining the variations in forest root biomass. The results of the analysis of root biomass per ha, annual increment of root biomass per ha, and ratio of root and above-ground in the seven forest types categorized by climate, soil, and stand characteristics may be used for accurately determining C sequestration by the forest root and estimating forest biomass in this region

MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images

As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

Cross-project defect prediction (CPDP) trains the prediction models with existing data from other projects (the source projects) and uses the trained model to predict the target projects. To solve two major problems in CPDP, namely, variability in data distribution and class imbalance, in this paper we raise a CPDP model combining feature transfer and ensemble learning, with two stages of feature transfer and the classification. The feature transfer method is based on Pearson correlation coefficient, which reduces the dimension of feature space and the difference of feature distribution between items. The class imbalance is solved by SMOTE and Voting on both algorithm and data levels. The experimental results on 20 source-target projects show that our method can yield significant improvement on CPDP

Comparative Study of Materials to SF6 Decomposition Components

In order to judge the inside insulation fault of SF6 insulated equipment, the gas-sensing properties to a series of characteristic SF6 decomposition components, SOF2, SO2F2, SO2, H2S, CF4, HF, and SF6, have been studied. In this study, a comparative study of these gas-sensing materials has been made in theoretical and experimental fields to find the optimal gas-sensing material, and put forward the effective approaches to improve the gas-sensing properties of materials

Diversity and soil chemical properties jointly explained the basal area in karst forest

IntroductionPlant diversity and soil chemical properties are important factors affecting the plant growth. We sought to compare the explanatory rates of diversity and soil chemical properties in explaining the variation of basal area in karst forests, and also sought to compare the relative importance of the niche complementarity and mass ratio hypotheses.MethodsOn the basis of linear regression and structural equation modelling, we examined the correlation between the basal area of plant communities and species diversity, functional diversity, phylogenetic diversity, the community-weighted mean (CWM) of traits, and soil chemical properties, using data obtained from 35 monitoring plots in southwest China.ResultsSpecies, functional, and phylogenetic diversities were all significantly correlated with the basal area of the plant community, among the indices of which, Faith’s phylogenetic diversity was found to have the greatest explanatory power for basal area. These plant diversity indices can better explain the variation in basal area than the CWM of traits, suggesting the niche complementarity hypothesis is more applicable than the mass ratio hypothesis. Moreover, soil chemical properties also have an equal important impact. Different chemical properties were found to show significant positive correlations with basal area, and their total effects on basal area were shown to be greater than the CWM of traits.DiscussionAttention should be paid to diversity and soil chemical properties. This study provides theoretical guidance for understanding biodiversity maintenance mechanisms and protecting karst forests

Seasonal Changes and Vertical Distribution of Fine Root Biomass During Vegetation Restoration in a Karst Area, Southwest China

In karst ecosystems, plants absorbing smaller amounts of nutrients, owing to shallow soil, show limited growth. In addition, fine roots (diameter < 2 mm) contribute to the regulation of nutrient cycles in terrestrial ecosystems. However, the spatial and temporal variations of fine root biomass in different vegetation types of the karst region remains poorly understood. In this study, we investigated the seasonal and vertical variation in biomass, necromass, and total mass of fine roots using sequential soil coring under different stages of vegetation restoration (grassland, shrubland, secondary forest, and primary forest) in Southwest China. The results showed that the fine root biomass and necromass ranged from 136.99 to 216.18 g m−2 and 47.34 to 86.94 g m−2, respectively. The total mass of fine roots and their production ranged from 187.00 to 303.11 g m−2 and 55.74 to 100.84 g m−2 year−1, respectively. They showed a single peak across the vegetation restoration gradient. The fine root biomass and total fine root mass also showed a single peak with seasonal change. In autumn, the fine root biomass was high, whereas the necromass was low. Most of the fine roots were concentrated in the surface soil layer (0–10 cm), which accounted more than 57% root biomass, and decreased with increasing soil depth. In addition, fine root production showed a similar vertical pattern of variation with biomass. Overall, our results suggested that fine roots show clear seasonal and vertical changes with vegetation succession. Moreover, there was a higher seasonal fluctuation and a greater vertical decreasing trend in late-successional stages than in the early-successional stages. The conversion of degraded land to forest could improve the productivity of underground ecosystems and vegetation restoration projects in the fragile karst region should, therefore, continue