@Article{cmes.2022.019996,
AUTHOR = {Ziwei Jiang, Shuaibing Li, Xiping Ma, Xingmin Li, Yongqiang Kang, Hongwei Li, Haiying Dong},
TITLE = {State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {132},
YEAR = {2022},
NUMBER = {1},
PAGES = {295--317},
URL = {http://www.techscience.com/CMES/v132n1/48098},
ISSN = {1526-1506},
ABSTRACT = {<p>The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid. To improve the prediction accuracy of power systems with source-load two-terminal uncertainties, an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix <i>Q</i>0 is proposed in this paper. In the algorithm, the <i>Q</i>0 is used to offset the modeling error, and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems. Verification of the proposed method is implemented on the IEEE 30 node system through simulation. The results show that, compared with the traditional methods, the improved adaptive cubature Kalman filter has higher prediction accuracy, which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.</p>
},
DOI = {10.32604/cmes.2022.019996}
}