| 引用本文: | 廖泉森,何 川,张鸿皓,等.基于短路电流约束学习的机理-数据驱动电力系统优化运行方法研究[J].电力系统保护与控制,2026,54(01):143-155.[点击复制] |
| LIAO Quansen,HE Chuan,ZHANG Honghao,et al.Research on mechanism-data-driven optimal operation method for power systems based on short-circuit current constraint learning[J].Power System Protection and Control,2026,54(01):143-155[点击复制] |
|
| 摘要: |
| 随着当前电力系统的发展，短路电流超标问题显著，单一限流措施难以满足系统安全运行要求。为此，提出一种基于短路电流约束学习的机理-数据驱动电力系统优化运行方法。首先，为面对电力系统中复杂的运行方式，提出线路投切、母线分段与机组启停的组合限流措施。其次，针对多层感知器(multi layer perceptron, MLP)模型学习拓扑变化时能力有限的问题，提出了一种基于One-hot编码的拓扑特征增强方法，以提升模型对限流措施的适应能力。再次，提出短路电流安全距离概念，以量化不同限流措施对短路电流约束的违反程度，并进一步采用大M法处理MLP的前向传播公式，建立基于数据驱动建模的短路电流约束。最后，以机组运行和拓扑调整总成本最小为目标，并考虑电网运行约束、N1约束与短路电流约束，建立基于短路电流约束学习的机理-数据驱动电力系统优化运行模型。并通过算例验证所提模型的有效性。 |
| 关键词: 短路电流 拓扑调整 机理-数据驱动 机器学习 约束学习 |
| DOI：10.19783/j.cnki.pspc.250301 |
| 投稿时间：2025-03-23修订日期：2025-07-15 |
| 基金项目:国家电网公司科技项目资助(5108-202218280A-2-263-XG) |
|
| Research on mechanism-data-driven optimal operation method for power systems based on short-circuit current constraint learning |
| LIAO Quansen1,HE Chuan1,ZHANG Honghao1,YE Xi2,SUN Xinwei3,WANG Biao2 |
| (1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China; 2. State Grid Sichuan Electric Power Company,
Chengdu 610041, China; 3. State Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610041, China) |
| Abstract: |
| With the ongoing development of modern power systems, short-circuit current limit violations have become increasingly prominent, and single current-limiting measures are no longer sufficient to ensure safe system operation. To address this issue, this paper proposes a mechanism-data-driven power system optimal operation method based on short-circuit current constraint learning. First, to cope with complex operation modes in power systems, a set of combined current-limiting measures, including line switching, busbar sectionalizing, and unit start/stop, is proposed. Second, to overcome the limited ability of multi layer perceptron (MLP) models in learning topology variations, a topology feature enhancement method based on One-hot encoding is proposed to improve the model’s adaptability to current-limiting measures. Third, the concept of short-circuit current safety margin is introduced to quantify the degree of violation of short-circuit current constraints under different current limiting measures. The big-M method is then employed to process the forward propagation formula of the MLP, thereby establishing data-driven short-circuit current constraints. Finally, with the objective of minimizing the total cost of unit operation and network topology adjustments, and considering grid operating constraints, N-1 security constraints, and short-circuit current constraints, a mechanical-data-driven power system optimal operation model based on short-circuit current constraint learning is established. Case studies verify the effectiveness of the proposed model. |
| Key words: short-circuit current topology adjustment mechanism-data-driven machine learning constraint learning |
