<![CDATA[This paper introduces an innovative application of the driving training-based optimization (DTBO) technique to optimize a multiple linear regression (MLR) model for estimating synchronous motor (SM) excitation current. Inspired by structured learning in driving training, DTBO is utilized to accurately determine regression coefficients with fast convergence. The DTBO-based MLR model is compared with other optimization techniques, such as gravitational search algorithm (GSA), artificial bee colony (ABC), genetic algorithm (GA), symbiotic organisms search (SOS), and various machine learning algorithms. Using a dataset of 557 samples (390 for training, 167 for testing), the DTBO-based model achieves the lowest objective function value, demonstrating superior performance in minimizing estimation errors. Key metrics like maximum error, error percentage, standard deviation, and root mean square error (RMSE) validate the results. The DTBO-based approach not only outperforms other methods but also provides a clear mathematical relationship between excitation current and input features, enabling easier hardware implementation and faster computation. This study establishes the DTBO-based MLR model as a robust and efficient alternative to complex machine learning algorithms for estimating SM excitation current, offering significant contributions to power systems engineering and smart grid applications.]]>
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