Original scientific paper
Condition based (re)investment in transformer populations
; DIDEE GmbH, DTC
APA 6th Edition
Daemisch, G. (2015). Condition based (re)investment in transformer populations. Transformers Magazine, 2 (1), 42-48. Retrieved from https://hrcak.srce.hr/182926
MLA 8th Edition
Daemisch, Georg. "Condition based (re)investment in transformer populations." Transformers Magazine, vol. 2, no. 1, 2015, pp. 42-48. https://hrcak.srce.hr/182926. Accessed 27 Sep. 2023.
Chicago 17th Edition
Daemisch, Georg. "Condition based (re)investment in transformer populations." Transformers Magazine 2, no. 1 (2015): 42-48. https://hrcak.srce.hr/182926
Daemisch, G. (2015). 'Condition based (re)investment in transformer populations', Transformers Magazine, 2(1), pp. 42-48. Available at: https://hrcak.srce.hr/182926 (Accessed 27 September 2023)
Daemisch G. Condition based (re)investment in transformer populations. Transformers Magazine [Internet]. 2015 [cited 2023 September 27];2(1):42-48. Available from: https://hrcak.srce.hr/182926
G. Daemisch, "Condition based (re)investment in transformer populations", Transformers Magazine, vol.2, no. 1, pp. 42-48, 2015. [Online]. Available: https://hrcak.srce.hr/182926. [Accessed: 27 September 2023]
Slowly but surely, condition-based maintenance is coming to be understood worldwide as the number-one choice for optimising the reliability and cost-effective service of transformers. Up to now, however, it has remained difficult to understand the actual condition of a transformer based only on traditional methods like water-in-oil or dissolved gas analysis (DGA) data. Also, widely used furan analysis for evaluating the paper degradation usually provides less than clear results. Many users try to work with any of the “standard methods” based on IEC or IEEE, or any other generalised method. Considering the problem of significantly different transformer designs and service data, this attempt will rarely be accurate and is usually doomed to fail. To this day, it remains in the realm of long-experienced specialists to truly understand the complete and complex data, and to also understand the defects and weakness of normally available values, and to finally come up with a reliable result which can be validly used as a basis for further maintenance decisions.
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