Hybrid Analytical-Machine Learning Framework for Ripple Factor Estimation in Cockcroft-Walton Voltage Multipliers with Residual Correction for Non-Ideal Effects

Abstract

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.