分析了传统LCC直流输电系统无功控制(reactor power control,RPC)功能在工程实现中存在的逻辑和电气设计隐患。针对无功控制中缺少交流滤波器投切异常监视、测量参数异常监视等功能,提出了一种无功控制的监视方法。监视方法在起动程序...分析了传统LCC直流输电系统无功控制(reactor power control,RPC)功能在工程实现中存在的逻辑和电气设计隐患。针对无功控制中缺少交流滤波器投切异常监视、测量参数异常监视等功能,提出了一种无功控制的监视方法。监视方法在起动程序内进行逻辑判断,根据结果进入正常运行程序或故障计算程序,实现了交流滤波器投切异常分析判断、交流滤波器投切状态判断、交流电压有效性检查、无功有效性检查、RPC震荡闭锁等功能,其中部分功能已运用于工程中,运行情况良好。展开更多
LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical su...LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.展开更多
文摘分析了传统LCC直流输电系统无功控制(reactor power control,RPC)功能在工程实现中存在的逻辑和电气设计隐患。针对无功控制中缺少交流滤波器投切异常监视、测量参数异常监视等功能,提出了一种无功控制的监视方法。监视方法在起动程序内进行逻辑判断,根据结果进入正常运行程序或故障计算程序,实现了交流滤波器投切异常分析判断、交流滤波器投切状态判断、交流电压有效性检查、无功有效性检查、RPC震荡闭锁等功能,其中部分功能已运用于工程中,运行情况良好。
文摘LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.
