scholarly journals Experimental Research on Power Transformer Vibration Distribution under Different Winding Defect Conditions

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 842
Author(s):  
Yiwei Hu ◽  
Jing Zheng ◽  
Hai Huang

Vibration analysis is one of the important tools for the transformer winding faults diagnosis. Previous researchers have proved that the vibration spatial distribution of the winding is significantly influenced by the winding defects for the open circuit condition. In order to study the effects of the loading current on the winding vibrations under different mechanical conditions, experiments were designed and operated on a three-phase transformer winding to analyze the winding vibration distribution under different winding defect cases. Further, to study to what extent the mechanical defects and the loading current influence characteristics of the vibration distribution on the tank, the tank vibration distribution under various winding defects and different loading currents were also measured and discussed. In addition, the possibility of detection of transformer winding faults based on tank vibration spatial distribution characteristics was also discussed.

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.


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