| 摘要: |
| 现代电力系统具有结构复杂、子系统联系紧密、运行态势复杂多变等特点。因此，传统基于单一时间断面的N-1分析结果不足以保证系统运行安全。以节点负荷预测为基础，提出了一种基于时间过程的、能够考虑负荷变化趋势的N-1分析及继发性故障预警与调整方法。该方法首先采用分层聚类方法对节点负荷予以聚合划分，并利用数据挖掘技术提取表征每个划分区段负荷特性的特征数据。然后对初始预想故障进行安全分析，对故障后状态及负荷随时间变化引发的继发性故障进行预警，并对不安全时段采用最优潮流制定调整措施。最后通过算例分析，验证了所提方法在分 |
| 关键词: 安全分析 时间过程 分层聚类 特征数据 最优潮流 |
| DOI：10.7667/j.issn.1674-3415.2013.21.016 |
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| 基金项目:中央高校基本科研业务专项资金支持项目(12MS20) |
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| Early warning and corrective control of secondary failure based on load tendency |
| MAO An-jia,YANG Hao |
| () |
| Abstract: |
| Modern power systems are featured with complicated structure, strong connection, and changeable operation patterns. Therefore, the result of traditional N-1 security analysis based on single study case is not sufficient to ensure the secure operation of the power system. Based on the forecasted nodal curve, this paper proposes a method of improved N-1 analysis, which is time process oriented and considers the load tendency, and hence provides a new way for early warnings and corrective control of subsequent fault. There are two steps in the method, firstly, a hierarchical clustering method is used to partition the nodal curve, and then the technology of data mining is used to extract the effective characteristic data that can represent the load variation of each time segment. Secondly, security analysis of initial contingency is carried out to perform early warnings for subsequent fault, which is triggered by the overloads caused by initial contingency and changeable load tendency. Adjustment measures for the unsecure time segment are developed with optimal power flow. To verify the feasibility and superiority of this method, a testing scenario is provided and the result shows the correctness. |
| Key words: security analysis time course hierarchical clustering characteristic data optimal power flow |
