基于代价敏感支持向量机和多变量决策树的分级自适应暂态电压稳定评估OA北大核心CSTPCD
Hierarchical Self-adaptation Transient Voltage Stability Assessment Based on Cost-sensitive SVM and Multivariate Decision Tree
为解决暂态电压稳定评估中失稳工况漏判率高的问题、提升多变量决策树(multivariate decision tree,MDT)应用能力,提出一种分级代价敏感多变量决策树(hierarchical cost sensitive multivariate decision tree,HCS-MDT)评估方法.基于可量测电气量时空联合拓展构建特征,利用改进经验风险的代价敏感支持向量机(cost sensitive support vector machines,CS-SVM)作为MDT内部节点分类器,生成解析式组合特征判稳规则作为可视化决策依据,并能有效减少失稳漏判;将分级自适应(hierarchical self-adaptation,HSA)准则融入 CS-MDT 中进行暂态电压稳定评估,在提升早期评估能力的同时有效保障评估准确率.暂态电压稳定仿真算例验证了所提方法的有效性.
In order to solve the problem of the high mis-judgment rate under the unstable conditions in the transient voltage stability assessment and improve the application of the multivariate decision tree(MDT),a hierarchical cost sensitive-multivariate decision tree(HCS-MDT)assessment is proposed.Based on the spatiotemporal joint expansion of the measurable electrical quantities to construct the features,the cost-sensitive support vector machine(CS-SVM)with the improved empirical risks is used as the internal node classifier of the MDT,and the analytic combined feature stability judgment rule is generated as a visual stability judgment basis,which may effectively reduce the instability misjudgment.The hierarchical self-adaptation(HSA)criterion is integrated into the CS-MDT for the assessment of the transient voltage stability,which effectively guarantees the assessment accuracy while improving the early assessment.The simulation example of the transient voltage stability verifies the effectiveness of the proposed method.
甄永赞;阮程
新能源电力系统国家重点实验室(华北电力大学), 北京市 昌平区 102206
动力与电气工程
暂态电压稳定时空特征代价敏感支持向量机多变量决策树分级自适应
transient voltage stabilityspatiotemporal featuresCS-SVMMDTHSA
《电网技术》 2024 (002)
778-788,中插65-中插67 / 14
国家重点研发计划项目(2021YFB2400800):"响应驱动的大电网稳定性智能增强分析与控制技术".Project Supported by the National Key Research&Development Program of China(2021YFB2400800):"Response-driven Intelligent Enhanced Analysis and Control for Bulk Power System Stability".
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