On-line dynamic state estimation of power systems
作者： Wenzhong Gao , Shaobu Wang , None
DOI: 10.1109/NAPS.2010.5619951
关键词:
摘要: Traditional state estimators based on steady system model cannot capture the dynamics of power very well because slow updating rate SCADA systems (several seconds). The emergence wide-area measurement offers new opportunity for developing more effective methods to monitor online. But due nonlinearity transition and observation equation, linearization Jacobian Matrix calculation are indispensible in existing estimation. This makes WAMS' high performance compromised by burdensome inaccuracy In order overcome drawbacks, this study tries develop an estimation method without calculation. Firstly, unscented transformation is introduced as calculate means covariances a random vector undergoing nonlinear transformation. Secondly, embedding Kalman Filter process, developed dynamic Finally, some simulation results presented showing accuracy easier implementation proposed method.
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