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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.19434 |
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| _version_ | 1866909973590573056 |
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| author | Islam, Md. Tanvirul |
| author_facet | Islam, Md. Tanvirul |
| contents | Cockcroft-Walton (CW) voltage multipliers suffer from output ripple that classical analytical models underestimate due to neglected non-idealities like diode drops and capacitor ESR, particularly in high-stage, low-frequency and heavy-load regimes. This paper proposes a hybrid framework that generates a comprehensive 324-case MATLAB/Simulink dataset varying stages (2-8), input voltage (5-25 kV), capacitance (1-10 μF), frequency (50-500 Hz) and load (6-60 MΩ), then trains a Random Forest model to predict residuals between simulated and theoretical peak-to-peak ripple. The approach achieves 70.6% RMSE reduction (131 V vs. 448 V) globally and 66.7% in critical regimes, with near-zero bias, enabling physically interpretable design optimization while outperforming pure ML in extrapolation reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Analytical-Machine Learning Framework for Ripple Factor Estimation in Cockcroft-Walton Voltage Multipliers with Residual Correction for Non-Ideal Effects Islam, Md. Tanvirul Systems and Control Cockcroft-Walton (CW) voltage multipliers suffer from output ripple that classical analytical models underestimate due to neglected non-idealities like diode drops and capacitor ESR, particularly in high-stage, low-frequency and heavy-load regimes. This paper proposes a hybrid framework that generates a comprehensive 324-case MATLAB/Simulink dataset varying stages (2-8), input voltage (5-25 kV), capacitance (1-10 μF), frequency (50-500 Hz) and load (6-60 MΩ), then trains a Random Forest model to predict residuals between simulated and theoretical peak-to-peak ripple. The approach achieves 70.6% RMSE reduction (131 V vs. 448 V) globally and 66.7% in critical regimes, with near-zero bias, enabling physically interpretable design optimization while outperforming pure ML in extrapolation reliability. |
| title | Hybrid Analytical-Machine Learning Framework for Ripple Factor Estimation in Cockcroft-Walton Voltage Multipliers with Residual Correction for Non-Ideal Effects |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.19434 |