A Physics-Informed Neural Network-Based Transient Overvoltage Magnitude Prediction Method for Renewable Energy Stations Under DC Blocking Scenarios

Abstract

Large-scale power systems typically require long-distance transmission of electrical energy, and high-voltage direct current (HVDC) technology is a commonly used high-capacity means of connecting power sources to load centres. When a blocking fault occurs in an HVDC transmission system based on line commutated converters (LCC), the sending-end system is prone to transient overvoltage (TOV) risks. This is especially severe in systems with large-scale renewable energy integration, where excessive TOV can lead to widespread disconnection of renewable energy units, seriously threatening the safe and stable operation of the power system. Therefore, predicting the TOV magnitude in renewable energy stations (RES) under DC blocking (DCB) scenarios is of great importance for maintaining system stability and facilitating emergency control decisions. This paper first derives an analytical expression for the TOV magnitude at critical nodes in the system caused by DCB faults. Subsequently, an analytical formula is developed to characterize the relationship between the multiple renewable energy stations short circuit ratio (MRSCR) and the transient voltage rise (TVR) at the point of common coupling (PCC) of RES. Based on this, a physics-informed neural network-based transient overvoltage magnitude prediction (PINN-TOMP) method for RES under DCB scenarios is proposed. The method introduces a regularization term for MRSCR into the loss function to ensure that the PINN model adheres to the physical laws and constraints governing the power system, thereby enhancing the prediction accuracy. Finally, the proposed method was tested on a real regional power system in China, and the results validated its effectiveness.