| 引用本文: | 王光耀,刘 俊,姚宏伟,等.考虑新能源场站相互影响的暂态过电压幅值预测方法[J].电力系统保护与控制,2026,54(04):1-13.[点击复制] |
| WANG Guangyao,LIU Jun,YAO Hongwei,et al.Transient overvoltage magnitude prediction method considering the mutual interactions among renewable energy stations[J].Power System Protection and Control,2026,54(04):1-13[点击复制] |
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| 摘要: |
| 为了有效评估多直流送端系统在直流闭锁故障场景下新能源场站的暂态过电压(transient overvoltage, TOV)风险，提出了一种考虑新能源场站相互影响的暂态过电压幅值预测方法。首先，推导了直流闭锁故障引起的送端系统中新能源场站并网点暂态过电压幅值解析表达式。然后，提出了表征新能源多场站短路比(multiple renewable energy stations short circuit ratio, MRSCR)与新能源场站并网点暂态过电压幅值之间关联关系的近似解析表达式。鉴于MRSCR可以量化新能源场站间的耦合程度(亦即相互影响程度)，在此基础上基于知识嵌入神经网络，提出了一种考虑新能源场站相互影响的直流闭锁场景下新能源场站暂态过电压幅值预测方法。该方法通过在损失函数中引入对MRSCR的正则化项，确保TOV幅值预测模型符合电力系统中的物理约束，从而提高预测结果的准确性。最后，在中国某地区实际电力系统上对所提方法进行了测试。实验结果表明，相较于传统TOV幅值预测方法，所提计及新能源场站相互影响的TOV幅值预测方法能够显著提升预测精度。 |
| 关键词: 暂态过电压 新能源多场站短路比 直流闭锁 知识嵌入神经网络 送端系统 |
| DOI：10.19783/j.cnki.pspc.250683 |
| 投稿时间：2025-06-26修订日期：2025-09-29 |
| 基金项目:国家自然科学基金项目资助(52177111) |
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| Transient overvoltage magnitude prediction method considering the mutual interactions among renewable energy stations |
| WANG Guangyao1,LIU Jun1,YAO Hongwei2,LIN Kaiwei1,LIU Jiacheng1,LIU Xiaoming1,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) |
| 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. |
| Key words: transient overvoltage multiple renewable energy stations short-circuit ratio DC blocking knowledge- embedded neural network sending-end system |
