Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks

TL;DR

This paper introduces a physics-informed neural network approach combined with optimization techniques to accurately model transformer temperatures and determine optimal sensor placement for enhanced monitoring.

Contribution

It extends PINNs for 2D thermal modeling of transformers and integrates them with mixed integer optimization for optimal sensor placement.

Findings

PINNs accurately model transformer heat diffusion in 1D and 2D.

The combined approach identifies optimal sensor locations.

Method improves temperature monitoring efficiency.

Abstract

Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then used to determine the optimal placement for temperature sensors inside the transformer under the constraint of a limited number of sensors, enabling efficient performance monitoring. The method consists of combining PINNs with Mixed Integer Optimization Programming to obtain the optimal temperature reconstruction inside the transformer. First, we extend our PINN model for the thermal modeling of power transformers to solve the heat diffusion equation from 1D to 2D space. Finally, we construct an optimal sensor placement model inside the transformer that can be applied to problems in 1D and 2D.