科研之友
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摘要
With the integration of the renewable energy-based power plants into the power system, and due to their volatile nature, the overloading of power transformers is inevitable for economical reasons. Under a specified loading condition, the operation of fans leads to increase the heat transfer coefficient on the air side of the cooling system resulting in decreasing of the temperature of the oil entering the winding, and consequently decreasing the winding temperature and increasing the lifetime of the transformer. Therefore, the healthy operation of fans should be guaranteed when a transformer is subjected to a loading condition. In this paper, a new online algorithm is proposed for detection of fan failures in power transformer based on the detection of changes in the estimated parameters of a thermal model; moreover, an allowed band is provided for triggering the alarm signal which is able to be applied on every oil-immersed transformer with AF cooling system. Furthermore, an empirical-based thermal model is proposed which uses different temperature-dependent thermal resistances corresponding to different heat transfer phenomena. Furthermore, the proposed algorithm is validated using the measured data of a 333 MVA ODAF and a 600 MVA OFAF power transformer including ambient temperature, load factor, number of operative pumps and fans, and top-oil temperature.
- 出版日期2017-6
