Ultra-high-frequency (UHF) partial discharge (PD) online monitoring is an effective way to inspect potential faults and insulation defects in power transformers. The construction of UHF PD online monitoring system is a challenge because of the high-frequency and wide-frequency band of the UHF PD signal. This paper presents a novel, intelligent sensor for UHF PD online monitoring based on a new method, namely a level scanning method. The intelligent sensor can directly acquire the statistical characteristic quantities and is characterized by low cost, few data to output and transmit, Ethernet functionality, and small size for easy installation. The prototype of an intelligent sensor was made. Actual UHF PD experiments with three typical artificial defect models of power transformers were carried out in a laboratory, and the waveform recording method and intelligent sensor proposed were simultaneously used for UHF PD measurement for comparison. The results show that the proposed intelligent sensor is qualified for the UHF PD online monitoring of power transformers. Additionally, three methods to improve the performance of intelligent sensors were proposed according to the principle of the level scanning method.

First-Principles Insight Into Au-Doped MoS 2 for Sensing C 2 H 6 and C 2 H 4 .

In this work, flake-flower NiO was successfully prepared via a facile hydrothermal method. The microstructure of the synthesized sample was characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). We find that the hierarchical flake-flower structure was assembled by numerous nanosheets with different size and shape. The fabricated sensor based on the obtained microstructure exhibited excellent gas sensing performance including high response, outstanding selectivity and stability toward 5 ppm CO at the optimal working temperature of 250 • C. A plausible gas sensing mechanism was given out to explain how the nanosheet assembly morphology affects the gas sensing performance of the flake-flower structure.

In recent years, increasingly severe wildfires have posed a significant threat to the safe and stable operation of transmission lines. Wildfire risk assessment and early warning have become an important research topic in power grid risk assessment. This study proposes a fire prediction model on the basis of the CatBoost algorithm to effectively predict the fire point. Five wildfire risk factors, including vegetation factors, meteorological factors, human factors, terrain factors, and land surface temperature, were combined using the feature selection method on the basis of the gradient boosting decision tree model and principal component analysis to achieve dimensionality reduction of redundant data and create a fire prediction model. The MODIS fire point product is used as the model evaluation data. The verification result uses the AUC value as the evaluation factor. The accuracy of the model is 0.82, and the AUC value is 0.83. The obtained fire point evaluation results are in good agreement with the actual fire points. Results show that this model can effectively predict the risk of wildfires.

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