微电网的统一物理信息神经网络框架及其在电压稳定性分析中的应用

This paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability of Chongqing power system (CPS). Despite numerous algorithms proposed to tackle this issue, accurately and reliably extracting the parameters of these remains a significant challenge. This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. The PINN difficulty is overcome in UPINN by configuring four strategies: feedback operator, GRU gating mechanisms, transfer operator with historic population, and modification factor with PPO aided reinforcement learning. UPINN models are trained iteratively to maximize parameters and reduce RMSE. UPINN accurately extracts parameters and describes the behavior of PV, wind, and energy storage equipment models as it converges towards optimal solutions through parameter adjustments and RMSE evaluations. The UPINN was implemented for real-time voltage stability monitoring of CPS. The results show that UPINN performs better than other neural network models in respect of accuracy and stability, demonstrating the effectiveness of improved strategies. Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.