Accurate mathematical modeling of electronic load harmonics using machine learning software

Abstract

This paper presents accurate mathematical models for several power system harmonics problems using an efficient machine learning tool, Eureqa. Three previously published research papers that included actual field measurements have been chosen to compare and assess the harmonics mathematical models presented in this paper. Following that, mathematical models of the output parameters as functions of the input parameters have been developed based on these data. For all these different research works and experiments, a total of 17 mathematical models have been built using basic curve fitting tools. Most of these proposed models couldn’t fit the experimental data appropriately. Considerable error is observed for several models. In this paper, all these 17 problems are formulated using Eureqa software utilizing the same data presented in the discussed research works. Very accurate fitting capability to the experimental data is achieved using Eureqa, where almost near zero error is reached for the majority of the proposed models. The maximum mean absolute error (MAPE) among all developed models was 0.13% as opposed to 24% for the models presented in the literature.