Abstract
The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.
Highlights
The health of power transformers is very important for the stable operation of power grid.There are a large number of transformers in service, most of which have been put into use for a certain period of time, and there will be internal faults in the long-term aging process
In view of the above problems, this paper proposes a transformer fault diagnosis method called Correlation
DBSCAN is an important method of clustering algorithm, but in transformer fault diagnosis, due to the difference of characteristic gas content used to distinguish each fault and the vagueness of their Euclidean distance distinction, the effect of DBSCAN method directly applied to fault diagnosis is not satisfying
Summary
The health of power transformers is very important for the stable operation of power grid.There are a large number of transformers in service, most of which have been put into use for a certain period of time, and there will be internal faults in the long-term aging process. It is of great significance to diagnose all kinds of latent faults in transformers accurately for the stable and normal operation of transformers. Dissolved gas analysis (DGA) is widely used in online diagnosis of oil-immersed transformers because it uses non-electric quantity as a reference and is not affected by electromagnetism [1,2]. The diagnosis process of DGA is generally divided into the extraction of transformer oil samples, the stripping of dissolved gas in oil, the measurement of gas components and the determination of fault category. The determination of fault category is the core process of DGA diagnosis. The central idea is to determine the fault category by the content of gas component in oil [3].
Sign-in/Register to view highlights & summary 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.
