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| Transient overvoltage magnitude prediction method considering the mutual interactions among renewable energy stations |
| DOI：10.19783/j.cnki.pspc.250683 |
| Key Words:transient overvoltage multiple renewable energy stations short-circuit ratio DC blocking knowledge- embedded neural network sending-end system |
| Author Name | Affiliation | | WANG Guangyao1 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | LIU Jun1 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | YAO Hongwei2 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | LIN Kaiwei1 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | LIU Jiacheng1 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | LIU Xiaoming1 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China | | GENG Shizhe3 | 1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. Taiyuan Power
Supply Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan 030001, China
3. State Grid Jibei Electric Power Company Limited, Beijing 100032, China |
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| Abstract:To effectively assess the transient overvoltage (TOV) risk of renewable energy stations in multi-DC sending-end systems under DC blocking fault scenarios, this paper proposes a TOV magnitude prediction method that considers the mutual interactions among renewable energy stations. First, an analytical expression for the TOV magnitude at the grid-connection points of renewable energy stations in the sending-end system caused by DC blocking faults is derived. Then, an approximate analytical expression is proposed to characterize the relationship between the multiple renewable energy stations short-circuit ratio (MRSCR) and the TOV magnitudes at the grid-connection points of renewable energy stations. Given that MRSCR can quantify the coupling degree (i.e., the mutual interaction) among renewable energy stations, a TOV magnitude prediction method for renewable energy stations under DC blocking scenarios is developed based on a knowledge-embedded neural network. By incorporating a regularization term associated with MRSCR into the loss function, the proposed model ensures that the TOV magnitude prediction adheres to the physical constraints of power systems, thereby improving prediction accuracy. Finally, the proposed method is validated on a practical power system in a region of China. The results demonstrate that, compared with conventional TOV magnitude prediction methods, the proposed method incorporating the mutual interactions among renewable energy stations can significantly enhance the prediction accuracy. |
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