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The main task of the state estimator in power systems is the provision of the power system state accurately. In order to achieve this, the measurement set has to be validated by identifying erroneous measurements. This is usually achieved by using either the Chi-square or the Largest Normalized Residual test. In both tests, the estimated states that are provided by the state estimator and the measurement...
Recently, many aspects of power system dynamic analysis, including oscillation identification, load model identification and system state estimation, are conducted based on the measurement data from phasor measurement unit (PMU). However, the impact of PMU measurement error, especially in power system dynamic process, has an assignable impact on these analysis results and thus cannot be neglected...
This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster.
In this paper, we compare two parameter estimation methods for distribution systems: 1) residual sensitivity analysis and 2) state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems;...
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