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    • 52. 发明授权
    • Adaptive Kalman Filtering in fault classification
    • 自适应卡尔曼滤波在故障分类中的应用
    • US4812995A
    • 1989-03-14
    • US52603
    • 1987-05-19
    • Adly A. GirgisRobert G. Brown
    • Adly A. GirgisRobert G. Brown
    • H02H3/40H02H7/26G06F15/20G01R31/08
    • H02H7/26H02H3/40
    • An Adaptive Kalman Filtering scheme for statistically predicting the occurence and type of a fault on a three phase power transmission line. Additionally, estimations of the steady-state postfault phasor quantities, distance protection and fault location information is provided. Current and voltage data for each phase is processed in two separate Adaptive Kalman Filtering models simultaneously. One model assumes that the phase is unfaulted, while the other model assumes the features of a faulted phase. The condition of the phase, faulted or unfaulted, is then decided from the computed a posteriori probabilities. Upon the secure identification of the condition of the phase, faulted or unfaulted, the corresponding Adaptive Kalman Filtering model continues to obtain the best estimates of the current or voltage state variables. Thus, the Adaptive Kalman Filtering model having the correct initial assumptions adapts itself to the actual condition of the phase faulted or unfaulted. Upon convergence of the computed a posteriori probabilities indicative of a faulted phase to highly accurate values, the type of fault is classified and the appropriate current and voltage pairs are selected to compute fault location and to provide distance protection. The voltage models are two state variable Adaptive Kalman Filtering schemes. The model for the current with no fault condition is two state variable, while the model that assumes that the phase is faulted is a three state variable model. Estimation convergence reached exact values within half a cycle and consequently, in the same time fault location was determined.
    • 一种用于统计预测三相输电线路故障发生和类型的自适应卡尔曼滤波方案。 另外,提供了稳态后故障相量的估计,距离保护和故障定位信息。 每个阶段的电流和电压数据同时在两个独立的自适应卡尔曼滤波模型中进行处理。 一个模型假设相位是未触发的,而另一个模型假定故障相位的特征。 然后根据计算的后验概率来确定相位,故障或未触发的状态。 在安全识别相位状态,故障或未故障时,相应的自适应卡尔曼滤波模型继续获得当前或电压状态变量的最佳估计。 因此,具有正确的初始假设的自适应卡尔曼滤波模型适应于相位故障或未故障的实际情况。 在将计算出的指示故障相位的后验概率收敛到高度精确的值之后,对故障类型进行分类,并选择适当的电流和电压对以计算故障位置并提供距离保护。 电压模型是两种状态变量自适应卡尔曼滤波方案。 没有故障状态的电流模型是两个状态变量,假设相位故障的模型是三态变量模型。 估计收敛在半个周期内达到精确值,因此在同一时间确定了故障位置。