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As electricity demand continues to grow and renewable energy increases its penetration in the power grid, real-time state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by phasor measurement units (PMUs). In this paper, we present a two-stage Kalman filter approach to estimate...
For decades, state estimation has been a fundamental aspect of power systems. However for large-scale and wide-area interconnected power systems, the required computation makes real-time on-line estimation a major challenge. In this paper we present a new method we call Lower Dimensional Measurement-space (LoDiM) state estimation. LoDiM is based on the Extended Kalman filter — popular because of its...
Applying Kalman filtering techniques to dynamic state estimation is a developing research area in modern power systems. Compared to traditional steady state estimators, the Kalman filter is able to track dynamic state variables both efficiently and accurately. However, in large-scale and wide-area interconnected power systems, the combination of computational complexity—primarily due to the very large...
Power system measurement devices continue to evolve towards higher accuracy and update rate. On the other hand, the computation required for processing the enormous amounts of measurement data associated with large complex power systems makes real-time estimation a major challenge. In this paper we present the Lower Dimensional Measurement-space (LoDiM) state estimation method for large-scale and...
The synchronized phasor measurement unit (PMU), developed in the 1980s, is considered to be one of the most important devices in the future of power systems. The recent development of PMU technology provides high-speed, precisely synchronized sensor data, which has been found to be useful for dynamic state estimation of the power grid. While PMU measurements currently cover fewer than 1% of the nodes...
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