Data-driven Modified Nodal Analysis Circuit Solver

TL;DR

This paper presents a novel data-driven modified nodal analysis circuit solver that eliminates the need for explicit component models by directly using measurement data to solve circuit problems, improving accuracy and reducing modeling errors.

Contribution

It introduces a reformulation of MNA that minimizes the distance to measurement data, removing the reliance on phenomenological models and their associated uncertainties.

Findings

Successfully applied to linear and nonlinear RC circuits

Effective in solving circuits with measurement-based data

Reduces modeling errors compared to traditional methods

Abstract

This work introduces a novel data-driven modified nodal analysis (MNA) circuit solver. The solver is capable of handling circuit problems featuring elements for which solely measurement data are available. Rather than utilizing hard-coded phenomenological model representations, the data-driven MNA solver reformulates the circuit problem such that the solution is found by minimizing the distance between circuit states that fulfill Kirchhoff's laws, to states belonging to the measurement data. In this way, the previously inevitable demand for model representations is abolished, thus avoiding the introduction of related modeling errors and uncertainties. The proposed solver is applied to linear and nonlinear RC-circuits and to a half-wave rectifier.