Application of Markov Model to Estimate Individual Condition Parameters for Transformers
| Autor: | Muhammad Sharil Yahaya, Mohd Aizam Talib, Norhafiz Azis, Jasronita Jasni, Amran Mohd Selva, Young Zaidey Yang Ghazali, Mohd Zainal Abidin Ab Kadir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
non-linear optimization
Control and Optimization Chi-square test 020209 energy Population Markov Model (MM) Condition-Based Monitoring (CBM) condition parameters estimation percentage of absolute error Energy Engineering and Power Technology 02 engineering and technology Population based Markov model 01 natural sciences lcsh:Technology law.invention law Approximation error 0103 physical sciences Statistics 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Transformer education Non linear optimization Engineering (miscellaneous) Mathematics 010302 applied physics education.field_of_study Renewable Energy Sustainability and the Environment lcsh:T Probability distribution Energy (miscellaneous) |
| Zdroj: | Energies, Vol 11, Iss 8, p 2114 (2018) Energies; Volume 11; Issue 8; Pages: 2114 |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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