Symbolic Equation Modeling of Composite Loads: A Kolmogorov-Arnold Network based Learning Approach

TL;DR

This paper introduces a novel learning approach using Kolmogorov-Arnold Networks to model composite loads with high accuracy and interpretability, addressing limitations of existing methods.

Contribution

It proposes a new symbolic equation modeling method that automatically derives interpretable load models without prior assumptions, improving flexibility and transparency.

Findings

Outperforms existing methods in accuracy and generalization

Automatically derives symbolic equations capturing nonlinear relationships

Provides transparent, interpretable load models

Abstract

With increasing penetration of distributed energy resources installed behind the meter, there is a growing need for adequate modelling of composite loads to enable accurate power system simulation analysis. Existing measurement based load modeling methods either fit fixed-structure physical models, which limits adaptability to evolving load mixes, or employ flexible machine learning methods which are however black boxes and offer limited interpretability. This paper presents a new learning based load modelling method based on Kolmogorov Arnold Networks towards modelling flexibility and interpretability. By actively learning activation functions on edges, KANs automatically derive free form symbolic equations that capture nonlinear relationships among measured variables without prior assumptions about load structure. Case studies demonstrate that the proposed approach outperforms other methods in both accuracy and generalization ability, while uniquely representing composite loads into transparent, interpretable mathematical equations.