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基于GLR-NT的显著误差检测与数据协调
蒋余厂, 刘爱伦
(华东理工大学自动化系)
Gross error detection and data reconciliation based on a GLR-NT combined method
jiangyuchang, liuailun
(Department of Automation, East China University of Science and Technology)
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投稿时间:2010-12-07    修订日期:2011-01-04
中文摘要: 介绍一种广义似然比法(generalized likelihood ratio, GLR)与节点检测法 (nodal test, NT)组合的显著误差检测和稳态数据协调方法。 充分发挥了GLR法和NT法的优点,采用逐次侦破、补偿校正的策略,避免了传统显著误差侦破方法中系数矩阵降秩问题,并且融入了测量变量的上下限约束,最终实现显著误差的侦破、识别、处理和测量数据的协调。仿真结果显示,该方法对多显著误差特别是误差幅度较小或出现节点大显著误差相互抵消的情况具有较好的性能,优于单独的GLR法和NT-MT法,一实例表明了算法的有效性。
Abstract:A GLR-NT combined method based on generalized likelihood ratio and nodal test is introduced for gross error detection and data reconciliation. The decrease of coefficient matrix rank was avoided and the gross error’s identification and processing, also the measurement data’s reconciliation were achieved by using a strategy of detecting and compensating in successive iteration with bounds constraint of measurement variables, making full use of the advantages of both generalized likelihood ratio and nodal test. The simulation results showed that the method could get better performance and was superior to both sole GLR method and NT-MT method for the system with more than one error, especially when the gross error’s magnitude was small or several biased stream were counteracted at the same node. Finally an actual example is provided to indicate the usefulness of the proposed method.
文章编号:20101207002     中图分类号:    文献标志码:
基金项目:国家高技术研究发展计划(863计划)
引用本文:
蒋余厂,刘爱伦.基于GLR-NT的显著误差检测与数据协调[J].华东理工大学学报(自然科学版),DOI:.

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