Abstract
This paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and computed condition parameters. It is found that estimation for majority of the individual condition parameter of the transformer population can be carried out by MM. The Chi-square test reveals that apart from CH4 and C2H4, the condition parameters are outside the rejection region that indicates agreement between predicted and computed values. It is also observed that the lowest and highest percentages of differences between predicted and computed values of all the condition parameters are 81.46% and 98.52%, respectively.
Highlights
Transformer is an integral component to ensure the reliability of power delivery in any utilities.monitoring its operational performance is very crucial to prevent failures
Health Index (HI) is a common tool used for Condition-Based Monitoring (CBM) purpose
It integrates all condition parameter data using a single quantitative index to represent transformer overall health status. This approach is useful to evaluate the long-term deterioration level based on the health condition that may not be viable to be identified by routine inspections and individual CBM techniques [4,5]
Summary
Transformer is an integral component to ensure the reliability of power delivery in any utilities. It integrates all condition parameter data using a single quantitative index to represent transformer overall health status This approach is useful to evaluate the long-term deterioration level based on the health condition that may not be viable to be identified by routine inspections and individual CBM techniques [4,5]. It addresses the interaction between parameter characteristics and attributes of these techniques which is not considered in the conventional method. MM based on individual condition parameter data is proposed to model the future deterioration of distribution transformer population.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
