[Example] Transformer winding temperature warning 'DTEmpower'
Early warning methods based on machine learning can respond more sensitively to abnormal conditions!
We would like to introduce a case study of applying the data modeling and analysis software "DTEmpower" to transformer winding temperature warnings. The machine learning method allows for a more sensitive detection of abnormal data points in winding temperature simply by setting the difference between the temperature measured by sensors and the temperature estimated by the model. Additionally, early warnings based on machine learning only require setting the degree of deviation from normal values, which essentially establishes a dynamic early warning zone. This approach offers greater flexibility and improved reliability compared to traditional static warning bands. 【Problems and Challenges】 - Ensuring the stability and reliability of transformers is a critical issue, and responses to failures need to be swift and effective. - The main cause of transformer failures is the decrease in insulation capacity. - To mitigate the risk of transformer failure due to decreased insulation capacity, early warnings for winding temperature are necessary. *For more details, please download the PDF or feel free to contact us.
basic information
**Benefits** - By simply setting the difference between the temperature measured by the sensor and the temperature estimated by the model, it can more sensitively detect abnormal data points in winding temperature. - Early warnings based on machine learning only require setting the degree of deviation (difference) from normal values. - Compared to traditional static warning bands, it offers greater flexibility and improved reliability. *For more details, please download the PDF or feel free to contact us.*
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For more details, please download the PDF or feel free to contact us.
