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| Adaptive Dynamic State Estimation Including Nonlinearities of Measurement Function in WAMS |
| Li Hong1, Li Weiguo1, Xiong Haoqing2 |
| 1. Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control Under Ministry of Education North China Electric Power University Beijing 102206 China 2. Research Center of Smart Grid, Electric Power of Henan Zhengzhou 450052 China |
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Abstract In this paper, two methods of modifying the model error covariance matrix are presented, and the measurement function nonlinearities are incorporated into the two adaptive dynamic state estimation models. That is, when the filter is conducted, the unknown or imprecise noise statistic characteristic is identified and modified on-line simultaneously. Additionally, since the nonlinearities of the measurement function are integrated into the state estimation models, linearized error yielding from the linearization of nonlinear power system is completely compensated, which greatly improves the adaptability of the proposed algorithms under various anomalies, such as sudden load change/drastic generation variation, bad data and topology error conditions. Besides, the voltage magnitudes and phase angles measured by phasor measurement unit (PMU) are introduced in the observed measurements, then the system redundancy is increased, and the filtering precision is improved as well. Simulation results show that the proposed algorithms have excellent forecasting and filtering performance under normal and various abnormal conditions.
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Received: 21 July 2009
Published: 04 March 2014
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2010, Vol. 25 
