Briefly describe the fuzzy theory in the diagnosis technology of oil immersed transformers
In transformer diagnostic technology, there are many theories and research findings that are currently being developed, such as expert systems, artificial neural network systems, and so on. Today, our manufacturer will introduce fuzzy theory to let you know what fuzzy theory is, its main principles, uncertainties, and areas worth learning.
The so-called fuzzy theory is to combine the uncertainty and fuzziness between fault phenomena, causes, mechanisms, and classifications in transformer fault diagnosis to reflect or solve the uncertainty and fuzziness of transformer faults through theoretical analysis.
In fact, there are two feasible directions for current research with promising development prospects. One is to combine existing technologies and fuzzy theory to provide better solutions for transformer faults; The second method is to use the self submission of fuzzy theory to achieve self-organizing and self-learning functions, similar to the popular AI nowadays.
Both of the above directions are feasible now, one has deep significance and the other can be quickly applied over time. After success, it can greatly improve the accuracy and stability of diagnostic systems. However, precisely because its name is fuzzy theory, it has inherent shortcomings, that is, its understanding of faults is still not accurate enough. There is no clear characteristic, and so far, it cannot accurately analyze the inevitable connection between these fuzzy fault causes. Therefore, with such a deficiency, I believe it can be developed better in the future and provide better fault solutions!
