基于中心性的结构-电气事故筛选与输电加固优先级排序
Centrality-based Structural-Electrical Contingency Screening and Transmission Reinforcement Prioritization
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中文总结 AI 辅助
本文提出一种混合结构-电气事故筛选方法，结合结构分析与潮流评估，在IEEE 39节点系统测试中识别出超加性线路对，可高效缩小事故与加固的搜索空间。
中文摘要 AI 辅助
快速事故筛选对于安全输电系统的运行与规划至关重要。尽管完整的交流事故分析能提供准确的系统评估，但对于大量事故集合而言，其计算开销极高。基于拓扑的筛选方法虽能减轻该负担，但可能无法准确反映与运行点相关的电气应力。此外，传统的单事故筛选既无法量化同时停运事件间的相互作用，也无法为加固优先级排序提供直接准则。本文提出一种混合结构-电气筛选方法，该方法将基于电抗加权中心性测度的、计算高效且与调度无关的结构分析，与基于线路停运分布因子的潮流严重度评估相结合。基于潮流严重度评估了两个指标：协同指标用于量化同时线路停运间的超加性相互作用，耐受指标用于根据加固候选者对事故后总严重度的降低程度进行排序。定量评估了结构指标与电气严重度间的对应关系。在IEEE 39节点系统上的测试识别出115个超加性线路对，超出5%阈值，其中最严重的相互作用超过单个停运效应线性组合的7倍。排名最高的加固措施将事故后平均热严重度降低了7.5%。结构排名与基于潮流的严重度间相关性有限，斯皮尔曼系数约为0.2。所提方法提供了一种高效的预筛选方法，可在详细的交流与经济评估前缩小事故与加固的搜索空间。
英文摘要
Rapid contingency screening is essential for secure transmission system operation and planning. Although full AC contingency analysis provides accurate system assessment, it requires high computational effort for large contingency sets. Topology-based screening methods reduce this burden but may not accurately represent operating-point-dependent electrical stress. In addition, conventional single-contingency screening does not quantify interactions among simultaneous outages or provide a direct criterion for reinforcement prioritization. This paper presents a hybrid structural-electrical screening approach that combines a computationally efficient, dispatch-independent structural analysis based on reactance-weighted centrality measures with a flow-based severity assessment using line outage distribution factors. Two indices are evaluated using the flow-based severity: a synergy index to quantify super-additive interactions among simultaneous line outages and a tolerance index to rank reinforcement candidates based on their reduction in aggregate post-contingency severity. The correspondence between structural indicators and electrical severity is evaluated quantitatively. Tests on the IEEE 39-bus system identify 115 super-additive line pairs beyond a 5% threshold, with the most severe interaction exceeding seven times the linear combination of individual outage effects. The highest-ranked reinforcement reduces the mean post-contingency thermal severity by 7.5%. The structural rankings show limited correlation with flow-based severity, with a Spearman coefficient of approximately 0.2. The proposed approach provides an efficient pre-screening method to reduce contingency and reinforcement search spaces before detailed AC and economic evaluations.
