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A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems
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DOI：10.1109/TIM.2020.3024338.png)
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Abstract
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The distribution of measurement noise is commonly considered as an assumed Gaussian model in power systems, but this assumption is not always true in reality. This article introduces a distributed maximum-likelihood-based state estimation approach for multiarea power systems using the student's t-distribution measurement noise model. The t-distribution has the property of thick tail to better model the occurrence of outliers and is fairly flexible to model different noise statistics. The finite-time average consensus algorithm is utilized in conjunction with an influence function to realize the proposed distributed approach within a totally distributed framework. Based on the local measurement residuals and the limited information exchanged with neighboring areas, each local area can obtain the global optimum system-wide robust state estimates, while the existing distributed state estimation methods can only get local estimates. Moreover, the communication scheme is more flexible and can be totally different from the transmission lines between local areas. Simulations tested on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed distributed approach.
Keywords:
Distributed state estimation
finite-time average consensus
influence function (IF)
non-Gaussian noise
t-distribution
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