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Power System Dynamic State Estimation Using Kalman Filtering Technique

机译:基于卡尔曼滤波技术的电力系统动态状态估计

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State estimation is an essential for online monitoring, control and security analysis of power systems. State estimator filters the information to get an accurate systems state. This paper presents Static and Dynamic state estimations. Weighted least square method is used for Static state estimation and Holt's exponential smoothing method as well as Kalman Filter Technique for dynamic state estimation. The accuracy of state estimation process is discussed based on the comparison of Kalman Filter technique with Holt's methods for various load conditions.
机译:状态估计对于电力系统的在线监视,控制和安全分析至关重要。状态估计器过滤信息以获得准确的系统状态。本文介绍了静态和动态状态估计。加权最小二乘法用于静态估计,而Holt指数平滑方法以及卡尔曼滤波技术用于动态估计。基于卡尔曼滤波技术与霍尔特方法在各种负载条件下的比较,讨论了状态估计过程的准确性。

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