A novel method of decision-making for power transformer maintenance based on failure-probability-analysis

2018 ◽  
Vol 13 (5) ◽  
pp. 689-695
Author(s):  
Hang Yang ◽  
Zhe Zhang ◽  
Xianggen Yin
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Edwell T. Mharakurwa ◽  
G. N. Nyakoe ◽  
A. O. Akumu

Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.


Nurse Leader ◽  
2021 ◽  
Author(s):  
Brooklyn Aaron ◽  
Avery Glover ◽  
Evelina Sterling ◽  
Stuart Downs ◽  
Jason Lesandrini

2014 ◽  
Vol 3 (2) ◽  
pp. 225-236 ◽  
Author(s):  
Alireza Salehi ◽  
Mohammad Izadikhah

2019 ◽  
Vol 57 ◽  
pp. 25-33 ◽  
Author(s):  
Aihua Liu ◽  
Ke Chen ◽  
Xiaofei Huang ◽  
Jieyun Chen ◽  
Jianfeng Zhou ◽  
...  

1992 ◽  
Author(s):  
J.F. Flory ◽  
J.W.S. Hearle ◽  
Richard Stonor ◽  
Yong Luo

2017 ◽  
Vol 22 (7) ◽  
pp. 2370-2383 ◽  
Author(s):  
Alireza Ghavidel ◽  
S. Roohollah Mousavi ◽  
Mohsen Rashki

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