A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems

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Abstract
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 paper introduces a distributed maximum-likelihood based state estimation approach for multi-area 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 finitetime average consensus algorithm is utilized in conjunction with 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.
Funding Information
  • National Natural Science Foundation of China (61903314)
  • Basic Research Program of Science and Technology of Shenzhen, China (JCYJ20190809162807421)
  • Natural Science Foundation of Fujian Province (2019J05020, 2018J01098)

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