Real-time thermal evaluation on three-phase enclosure gas insulated bus by PSO-PINN approach

Real-time thermal evaluation including temperature field calculation and internal heat source inversion is crucial for power equipment ampacity and safety. This study presents a particle swarm optimized physics-informed neural network (PSO-PINN) for thermal evaluation on three-phase enclosure gas insulated bus (GIB) unit. Computational fluid dynamics (CFD) simulations are conducted to generate two-dimensional sampling points for model training and testing. Fluid flow velocity and temperature at the sampling points are predicted by fully connected neural network (FCNN) with spatial coordinates as inputs, in which PSO strategy is utilized to determine the optimal width and depth of network architecture. Navier-Stokes (N-S) equations are incorporated into loss functions via differential operations of network outputs; besides multi-loss function balancing strategy is adopted to balance weights of different loss terms. The trained PSO-PINN model predicts the temperature field within 0.11 seconds, achieving a maximum relative error of less than 2.7% when compared against physical experiments and reference CFD simulations. The proposed model can also invert the internal heat source with an error of 2.83% using tank surface observation data contaminated with 10% noise. With natural incorporation of field-observed data, instantaneous inference time and measurement noise resistance, this work proves to be a promising tool for real-time digital twin (DT) thermal management of GIB units.