To optimize the PINN training process and reduce the computational burden for seamless integration into power system simulations, several strategies can be employed. Firstly, leveraging transfer learning techniques can accelerate the training of PINNs by initializing the network with pre-trained weights from similar tasks. This approach can help reduce the training time and computational resources required for convergence. Secondly, implementing advanced optimization algorithms such as adaptive learning rates and batch normalization can enhance the training efficiency of PINNs. These techniques can improve the convergence speed and stability of the training process. Additionally, exploring parallel computing and distributed training methods can further speed up the training of PINNs by utilizing multiple processing units simultaneously. By optimizing the training process, the integration of PINNs into power system simulations can be made more efficient and effective.