Hybrid Machine Learning and Mechanistic Thermal Model of Synchronous Generator
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Full text available on 2022-05-19
Overheating of synchronous generators may result in shortened generator lifespan, thus strict constraints are imposed on their operation. A dynamic model of the generator temperature may leave good monitoring of the generator condition, and also more flexible operation. Within the past, the mix of a thermal model of an air-cooled generator with better control has been considered to assist ride-through problems: By using a model-based online monitoring , the temperature development in certain locations within the synchronous generator were kept in restraint. Additionally, exploiting the generator’s full thermal capacity led to improved performance . Further work has considered various improved thermal generator models, together with model fitting and state estimation . Now also, the studies thus far have used normal, counter-current heat exchanger models with constant Stanton numbers, which authorize for an analytic, explicit heat exchanger models description. In , a heat exchanger model with temperature-dependent heat capacities was considered. The result's a two-point boundary value problem that's several thousand-fold slower to resolve than with a relentless Stanton number. To hurry up the solution, a nonlinear regression model was trained off-line to suit the solution of the boundary value problem. However, what is missing in  is that the possibility to think about heat exchangers with varying heat transfer coefficients. The thermal model of an air-cooled synchronous generator created in  and enlarged in  with a more realistic temperature-dependent condition was continued during this thesis, with variable heat transfer coefficients, to lower the time it takes a heat exchanger model to resolve a temperature-dependent problem. A hybrid model was created with estimated parameters from a data-driven model for a spread of scenarios, and also the performance was compared to the numeric solution, which was around 220 times quicker.