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An ensemble Kalman filter (EnKF) method is proposed to track dynamic states of generators. The algorithm of the EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies...
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...
Large-scale renewable resources and novel smart-grid technologies continue to increase the complexity of power systems. As power systems continue to become more complex, accurate modeling for planning and operation becomes a necessity. Inaccurate system models would result in an unreliable assessment of system security conditions and could cause large-scale blackouts. This motivates the need for model...
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