A Method For Estimating A Physical Quantity Of A Static Electric Induction Device Assembly

Abstract: The present invention relates to a method for estimating a physical quantity of a static electric induction device assembly (10). The static electric induction device assembly (10) comprises an enclosure (14), a static electric induction device (12) and a liquid (18) whereby the enclosure (14) accommodates the static electric induction device (12) and the liquid (18) such that the static electric induction device (12) is at least partially, preferably fully, submerged into the liquid (18). The method comprising using measured temperature data obtained from a measurement assembly (20). The measured temperature data comprises a temperature (Ttrue(x, t)) in each one of a plurality of different locations (x) of the static electric induction device assembly (10) as a function of time (t) for a reference time range (?Tref) when the static electric induction device assembly (10) is in a condition in which at least a portion of the static electric induction device (12) generates heat during at least a portion of the reference time range (?Tref). The method further comprises: — using a time dependent partial differential equation representing a physical condition of the static electric induction device assembly (10) during the reference time range (?Tref), wherein the physical quantity forms a source term of the partial differential equation; — generating a temperature model for estimated temperature data, the estimated temperature data corresponding to an estimated temperature (Test(x, t) ) in each one of the plurality of different locations (x) of the static electric induction device assembly (10) as a function of time (t), the temperature model comprising a first neural network (NN1) representing the estimated temperature data (Test(x, t)) as well as the measured temperature data (Ttrue(x, t)), and — estimating the physical quantity by training a neural network system that uses at least the following entities: the time dependent partial differential equation, information from the temperature model and a second neural network (NN2) for the physical quantity.

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