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

TL;DR

This paper introduces a hybrid analytical-machine learning approach to accurately estimate ripple in Cockcroft-Walton voltage multipliers, accounting for non-ideal effects and improving prediction accuracy over traditional models.

Contribution

It develops a comprehensive dataset and trains a Random Forest model to predict residual ripple, enhancing accuracy and interpretability in non-ideal conditions.

Findings

70.6% reduction in RMSE globally

66.7% reduction in critical regimes

Near-zero bias in predictions

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 {\mu}F), frequency (50-500 Hz) and load (6-60 M{\Omega}), 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.