Evaluating Different Machine Learning Techniques as Surrogate for Low Voltage Grids

TL;DR

This paper compares various machine learning algorithms to develop surrogate models for low voltage power grids, demonstrating that linear regression and neural networks perform best across different grid configurations and conditions.

Contribution

It provides a comparative analysis of multiple ML techniques as surrogates for low voltage grids, extending previous work by testing different topologies and dynamic conditions.

Findings

Linear regression and neural networks outperform other models.

Surrogate model quality remains stable despite grid topology changes.

Adding volatile energy sources and phase variability does not impair model accuracy.

Abstract

The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i.e., data-driven approximations of (sub)systems. In a recent work, a surrogate model for a low voltage grid was built using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these question. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.