Drive System Inverter Modeling Using Symbolic Regression
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- @Article{glucina:2023:Electronics,
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author = "Matko Glucina and Nikola Andelic and Ivan Lorencin and
Sandi {Baressi Segota}",
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title = "Drive System Inverter Modeling Using Symbolic
Regression",
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journal = "Electronics",
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year = "2023",
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volume = "12",
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number = "3",
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pages = "Article No. 638",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2079-9292",
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URL = "https://www.mdpi.com/2079-9292/12/3/638",
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DOI = "doi:10.3390/electronics12030638",
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abstract = "For accurate and efficient control performance of
electrical drives, precise values of phase voltages are
required. In order to achieve control of the electric
drive, the development of mathematical models of the
system and its parts is often approached. Data-driven
modelling using artificial intelligence can often be
unprofitable due to the large amount of computing
resources required. To overcome this problem, the idea
is to investigate if a genetic
programming–symbolic regressor (GPSR) algorithm
could be used to obtain simple symbolic expressions
which could estimate the mean phase voltages (black-box
inverter model) and duty cycles (black-box compensation
scheme) with high accuracy using a publicly available
dataset. To obtain the best symbolic expressions using
GPSR, a random hyperparameter search method and 5-fold
cross-validation were developed. The best symbolic
expressions were chosen based on their estimation
performance, which was measured using the coefficient
of determination (R2), mean absolute error (MAE), and
root mean squared error (RMSE). The best symbolic
expressions for the estimation of mean phase voltages
achieved R2, MAE, and RMSE values of 0.999, 2.5, and
2.8, respectively. The best symbolic expressions for
the estimation of duty cycles achieved R2, MAE, and
RMSE values of 0.9999, 0.0027, and 0.003, respectively.
The originality of this work lies in the application of
the GPSR algorithm, which, based on a mathematical
equation it generates, can estimate the value of mean
phase voltages and duty cycles in a three-phase
inverter. Using the obtained model, it is possible to
estimate the given aforementioned values. Such
high-performing estimation represents an opportunity to
replace expensive online equipment with a cheaper, more
precise, and faster approach, such as a GPSR-based
model. The presented procedure shows that the symbolic
expression for the accurate estimation of mean phase
voltages and duty cycles can be obtained using the GPSR
algorithm.",
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notes = "also known as \cite{electronics12030638}",
- }
Genetic Programming entries for
Matko Glucina
Nikola Andelic
Ivan Lorencin
Sandi Baressi Segota
Citations