Transient Overvoltage Prediction Method for Renewable Energy Stations via Knowledge-Embedded Enhanced Deep Neural Network

Guangyao Wang, Jun Liu (), Jiacheng Liu, Yuting Li, Tianxiao Mo and Sheng Ju
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Guangyao Wang: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jun Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jiacheng Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yuting Li: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Tianxiao Mo: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Sheng Ju: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2025, vol. 18, issue 5, 1-18

Abstract: When a line-commutated converter–high-voltage direct current (LCC-HVDC) transmission system with large-scale integration of renewable energy encounters HVDC-blocking events, the sending-end power system is prone to transient overvoltage (TOV) risks. Renewable energy units that are connected via power electronic devices are susceptible to large-scale cascading disconnections due to electrical endurance and insulation limitations when subjected to an excessively high TOV, which poses a serious threat to the safe and stable operation of the system. Therefore, the prediction of TOV at renewable energy stations (RES) under DC blocking (DCB) scenarios is crucial for developing strategies for the high-voltage ride-through of renewable energy sources and ensuring system stability. In this paper, an approximate analytical expression for the TOV at RES under DCB fault conditions is firstly derived, based on a simplified equivalent circuit of the sending-end system that includes multiple DC transmission lines and RES, which can take into consideration the multiple renewable station short-circuit ratio (MRSCR). Building on this, a knowledge-embedded enhanced deep neural network (KEDNN) approach is proposed for predicting the RES’s TOV for complex power systems. By incorporating theoretical calculation values of the TOV into the input features, the task of the deep neural network (DNN) shifts from mining relationships within large datasets to revealing the correlation patterns between theoretical calculations and real values, thereby improving the robustness of the prediction model in cases of insufficient training data and irrational feature construction. Finally, the proposed method is tested on a real-world regional power system in China, and the results validate the effectiveness of the proposed method. The approximate analytical expression for the TOV at RES and the KEDNN-based TOV prediction method proposed in this paper can provide valuable references for scholars and engineers working in the field of power system operation and control, particularly in the areas of overvoltage theoretical calculation and mitigation.

Keywords: transient overvoltage; DC blocking; deep neural network; multiple renewable station short-circuit ratio (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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