Back to sections

Parameters Optimization and Model Fitting of Thermal Models of Air-cooled Hydrogenerator

Abstract

Parameters optimization plays a vital role for fitting
the mathematical model with the experimental data. In the prior
model development, some of the parameters are either chosen
based on an educated guess or hit and trial method or even
at random. This cause mathematical model to drift away with
experimental data. The optimized value of parameters, for the
fitting of mathematical models with experimental data, can be
found formulating a least-squares data fitting problem. In this
paper parameters of the four quasi-linear thermal models of
the air-cooled hydrogenerator are optimized formulating a least-
squares data fitting problem. We have formulated the data fitting
problem by using two different measurement data vectors. First,
we have used measurement data vector containing measurement
of two of the states for finding optimized parameters and second,
we have used measurement data vectors containing two of
the states and two of the algebraic variables. The optimized
parameters found by using two different measurement data
vectors are then used for fitting mathematical models with
experimental data. The performance of model fitting is then
compared using root mean square errors (RMSE) of least square
errors. We found that the choice of data affects for better model
fitting.

Authors

• Madhusudhan Pandey (University of South-Eastern Norway)
• Bernt Lie (University of South-Eastern Norway)
• Thomas Øyvang (University of South-Eastern Norway)