Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
This work addresses sensor placement optimization for power transformer monitoring, which is an incremental application of existing PINN methods to a specific engineering domain.
The paper tackles the problem of predicting temperature conditions in power transformers using Physics-Informed Neural Networks (PINNs) to determine optimal sensor placement under limited sensor constraints, achieving efficient performance monitoring through a method combining PINNs with Mixed Integer Optimization Programming.
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.