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Title: Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs

Abstract

This paper proposes a correlation-aided robust adaptive unscented Kalman filter for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKFUI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that the proposed robust AUKF-UI achieves much better results than other methods in the presence of low measurement redundancy, strong nonlinearity, and bad data.

Authors:
 [1];  [2];  [3];  [3];  [4];  [5]; ORCiD logo [3]
  1. Virginia Polytechnic Institute
  2. Southwest Jiaotong University
  3. BATTELLE (PACIFIC NW LAB)
  4. VISITORS
  5. Virginia Tech
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1710217
Report Number(s):
PNNL-SA-144597
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 35; Journal Issue: 3
Country of Publication:
United States
Language:
English

Citation Formats

Zhao, Junbo, Zheng, Zongsheng, Wang, Shaobu, Huang, Renke, Bi, Tianshu, Mili, Lamine, and Huang, Zhenyu. Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs. United States: N. p., 2020. Web. doi:10.1109/TPWRS.2019.2953256.
Zhao, Junbo, Zheng, Zongsheng, Wang, Shaobu, Huang, Renke, Bi, Tianshu, Mili, Lamine, & Huang, Zhenyu. Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs. United States. https://doi.org/10.1109/TPWRS.2019.2953256
Zhao, Junbo, Zheng, Zongsheng, Wang, Shaobu, Huang, Renke, Bi, Tianshu, Mili, Lamine, and Huang, Zhenyu. Fri . "Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs". United States. https://doi.org/10.1109/TPWRS.2019.2953256.
@article{osti_1710217,
title = {Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs},
author = {Zhao, Junbo and Zheng, Zongsheng and Wang, Shaobu and Huang, Renke and Bi, Tianshu and Mili, Lamine and Huang, Zhenyu},
abstractNote = {This paper proposes a correlation-aided robust adaptive unscented Kalman filter for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKFUI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that the proposed robust AUKF-UI achieves much better results than other methods in the presence of low measurement redundancy, strong nonlinearity, and bad data.},
doi = {10.1109/TPWRS.2019.2953256},
url = {https://www.osti.gov/biblio/1710217}, journal = {IEEE Transactions on Power Systems},
number = 3,
volume = 35,
place = {United States},
year = {2020},
month = {5}
}