Dynamic state estimation of power systems with quantization effects: a recursive filter approach
In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link
Citation : L. Hu., Wang, Z. and Liu, X. (2016) Dynamic state estimation of power systems with quantization effects: a recursive filter approach. IEEE Transactions on Neural Networks and Learning Systems, 27(8), pp. 1604--1614.
ISSN : 2162-237X
Research Group : DIGITS
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes