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Load models are commonly approximated by fixed impedance type loads when formulating dynamic simulations. This is an acceptable approximation when using dynamic simulation for a specific initial operating condition and the study runs for a brief period typically to check system stability. When implementing an on-line dynamic estimator such assumptions may not be viable due to the load dynamics which...
The complexity of power systems continue to increase as load demands grow and new energy technologies emerge. Efficient methodologies and instrumentation are needed for real-time monitoring and control of power systems. Accurately tracking the state variables (rotor angle and speed) is necessary for monitoring system stability conditions and assessing the risks of large-scale system collapse. Previous...
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...
This paper presents a new approach for wide area dynamic monitoring of the system with many possible applications. One such application is discussed to provide real time stability controls. The new approach utilizes a substation based dynamic state estimation. The substation based dynamic state estimation uses data from relays, PMUs, meters, FDRs etc in the substation only thus avoiding all issues...
This paper provides a methodology to extract the dynamic real time model of an electric power system using phasor measurement unit (PMU) data (GPS-synchronized) and other SCADA data that are available in substations. In addition to typical voltage and current measurements, PMU data include frequency and rate of change of frequency. Such data are available in raw form, as time-stamped instantaneous...
The state estimation tools which are currently deployed in power system control rooms are based on a quasi-steady-state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper presents an overview of the Kalman filtering process and then focuses on the implementation...
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