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CHAPTER 3: HF MODELLING STRATEGY
Defining the bounds for the fitting
A key role for the optimization process is played by the bounds given to each parameter
so that they can be selected within reasonable limits. This will facilitate the optimiza-
tion’s convergence, saving computational time, plus it will hinder the process being
trapped in local minima. On the other hand, too narrow limits can lead to wrong solu-
tions being selected, above all where the final values are saturated toward the chosen
boundaries. Unfortunately there is no general rule on how to determine the boundary
values, even so a great role is played by experience when, according to the topology,
there is already an idea of the range of the various components of the model. An exam-
ple could be if capacitors have to be modelled, it is expected that the main capacitance
will be close to the nominal value and the associated series resistance will be low.
To find the range for the HF parameters values in the motor model optimization, given
the number of parameters to be selected, some initial runs of GA have been launched
with wide bounds, and then this range has been narrowed around the more recurrent
values.
3.4 HF model simulations
A key point for the HF impedance simulation combined with the GA is the computa-
tional speed and the versatility of the model to be quickly modified if needed. The first
approach used was to iteratively run a circuit Saber simulation within the Matlab GA
script, where the value of every component, for each iteration, was passed to the Saber
simulator. This approach implied an understanding of how to externally execute a se-
ries of repetitive steps with the Synopsys tools: launch Saber Simulator, run a script to
alter the component’s values, execute the frequency analysis, close the simulator and
open the Cosmoscope to import the results, export them into a file and eventually im-
port these results into Matlab’s Workspace for the error calculation. This approach was
very powerful due to the interface with a proper circuit simulator; the problem, how-
ever, was the time needed to execute all of those steps that could not allow more than
a few simulations per minute. Because the GA needs hundred or possibly thousands
of iterations, it is clear that this method was not suitable and needed to be changed.
To fully take advantage of Matlab’s speed in handling matrices, a set of the state space
equations for the HF motor model has been extrapolated; this approach provided the
fastest computational speed, but was very poor in terms of versatility of the model.
With this approach, for every configuration, a different set of equations had to be calcu-
lated again for common and differential mode plus one for the parasitic measurement.
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