考虑灾前加固信息的配电网故障区段定位研究

doi:10.19595/j.cnki.1000-6753.tces.251961

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摘要

现有配电网故障区段定位方法在馈线终端单元(FTU)大规模失效时容错性显著下降,且既有灾前加固研究普遍缺乏与区段定位环节的协同。为此,本文提出一种灾前加固与故障区段定位协同的两阶段随机规划模型。首先,设计一种时序衔接的协同决策策略,通过加固措施提升信息感知可靠性并缩小故障搜索范围,以系统总期望成本最小化为目标实现两阶段协同优化。其次,基于上述决策策略建立协同优化模型：第一阶段确定馈线区段与FTU节点的加固方案并计算投资成本;第二阶段构建多源信息融合的区段定位模型,计算定位偏差惩罚。最后,将模型通过逻辑-代数转换转化为混合整数线性规划问题并高效求解。基于14节点配电网的仿真结果表明,仅加固2个关键区段与6个FTU节点,即可将定位准确率从45%提升至91%,验证了所提模型在有限预算下显著提升故障区段定位能力与系统韧性。

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关键词 ：极端灾害, 弹性配电网, 区段定位, 灾前加固, 两阶段随机规划

Abstract：

In modern society, extreme natural disasters pose severe challenges to the secure operation of power systems. Enhancing the resilience of distribution networks, enabling them to withstand and absorb the impact of disasters and quickly restore power supply, has become a core issue in power system planning and operation. Accurate fault section location is a key prerequisite for achieving rapid fault isolation, power supply restoration, and enhancing system resilience. However, existing research still exhibits significant shortcomings in coping with large-scale disasters: On one hand, most fault location methods heavily rely on information uploaded by feeder terminal units (FTUs). Once disasters cause widespread FTU failures or information distortion, their fault tolerance performance sharply declines, easily leading to location failures or misjudgments. On the other hand, most pre-disaster reinforcement studies focus on improving the physical disaster resistance of lines, failing to synergize with the information sensing and processing in fault location, resulting in suboptimal resource allocation efficiency.
To address these issues, this paper proposes a two-stage stochastic programming model that integrates pre-disaster reinforcement with fault location. The core contribution of this research lies in designing a sequential coordinated decision-making strategy: The first stage is pre-disaster planning, which involves targeted reinforcement of line sections and FTU nodes to directly enhance information sensing reliability while narrowing down the potential fault search range from the source, creating favorable conditions for subsequent location. This strategy aims to minimize the total expected cost over the system's lifecycle, achieving global-level synergy between pre-disaster reinforcement investment and post-disaster fault misjudgment costs. Based on this strategy, this paper constructs a corresponding collaborative optimization model. The first stage, under a given budget constraint, decides which line sections and FTU nodes to reinforce and calculates the corresponding investment costs. The second stage establishes a fault section location model based on multi-source information fusion, comprehensively considering fault indication information uploaded by FTUs that may be damaged by disasters, and making integrated judgments through an information fusion algorithm. To quantify location performance, a location deviation penalty mechanism is introduced, calculating misjudgment costs based on the deviation between the location result and the actual fault position. The second stage simulates a large number of random disaster scenarios to compute the expected penalty cost, feeding this performance back to the reinforcement decisions in the first stage. The model also considers complex conditions such as budget constraints and fault multiplicity. By linearizing nonlinear constraints and objective functions, the optimization problem is ultimately transformed into a Mixed-Integer Linear Programming (MILP) problem that can be efficiently solved.
Simulation results on a 14-node test system show: (1) With a budget of 100,000 yuan, reinforcing only 2 line sections and 6 FTU nodes can significantly increase the average fault location accuracy from 45% to 91%. (2) This method effectively balances reinforcement at the physical and information layers: FTU reinforcement directly improves data quality, while line section reinforcement indirectly simplifies the location problem by reducing the probability of complex multiple faults. (3) Sensitivity analysis indicates that the optimal reinforcement strategy is determined by both investment costs and fault risks: In low-risk scenarios, information layer reinforcement can be prioritized to save costs; in high-risk scenarios with frequent multiple faults, both physical and information layer reinforcement are required, which, although more costly, significantly enhances system resilience. (4) The model is also validated on a 33-node system, where location accuracy increases from 39% to 75% under the corresponding budget, demonstrating good scalability and engineering application potential.

Key words： Extreme disaster resilient distribution networks section location pre-disaster reinforcement two-stage stochastic programming

收稿日期: 2025-11-17

PACS:

TM73

基金资助:

国家自然科学基金资助项目（52307109）

通讯作者: 谭洪男,1991年生,博士,讲师,研究方向为综合能源系统优化运行与规划、电力市场等。E-mail：tanhong@ctgu.edu.cn

作者简介: 王秋杰男,1988年生,博士,副教授,研究方向为综合能源系统优化运行与规划、弹性配电网等。E-mail：wangqiujie@ctgu.edu.cn

引用本文:

王秋杰, 李达, 谭洪, 随权, 廖伟. 考虑灾前加固信息的配电网故障区段定位研究[J]. 电工技术学报, 0, (): 35-. Wang Qiujie, Li Da, Tan Hong, Sui Quan, Liao Wei. Research on Fault Section Location in Distribution Networks Considering Pre-Disaster Reinforcement Information. Transactions of China Electrotechnical Society, 0, (): 35-.

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