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- W4386411339 abstract "Inverter drives play a crucial role in various industrial applications, where optimizing their parameters is essential for enhancing efficiency and performance. This work presents an advanced technique to optimize the DC Link Voltage (u_dc_k) parameter in inverter drives using machine learning techniques. Traditional methods of parameter optimization have faced challenges in terms of complexity and adaptability. This work proposes a data-driven approach that leverages machine learning models, including linear regression, decision tree, and random forest, to predict the optimized u_dc_k values. The dataset, comprising multiple features related to inverter drives, undergoes pre-processing to handle missing data and scale features. The performance of the machine learning models is evaluated using the root mean squared error (RMSE) metric, and k-fold cross-validation is employed for model comparison. Our results demonstrate the effectiveness of machine learning techniques in predicting optimized u_dc_k values, with the best-performing model yielding improved performance compared to traditional methods." @default.
- W4386411339 created "2023-09-05" @default.
- W4386411339 creator A5052027045 @default.
- W4386411339 date "2023-07-14" @default.
- W4386411339 modified "2023-10-18" @default.
- W4386411339 title "Machine Learning-Driven Optimization of Inverter Drive Parameters for Enhanced Electric Vehicle Efficiency: An In-Depth Analysis and Application" @default.
- W4386411339 cites W2753811353 @default.
- W4386411339 doi "https://doi.org/10.1109/wconf58270.2023.10235206" @default.
- W4386411339 hasPublicationYear "2023" @default.
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