New Kalman Filter Approach Exploiting Frequency Knowledge for Accurate PMU-Based Power System State Estimation

2020 ◽  
Vol 69 (9) ◽  
pp. 6713-6722 ◽  
Author(s):  
Carlo Muscas ◽  
Paolo Attilio Pegoraro ◽  
Sara Sulis ◽  
Marco Pau ◽  
Ferdinanda Ponci ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
State Estimation ◽  
System State ◽  
Power System State Estimation ◽  
Filter Approach

scholarly journals Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 293 ◽  
Author(s):  
Zhiyu Zhang ◽  
Jinzhe Qiu ◽  
Wentao Ma
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
System State ◽  
Power System State Estimation ◽  
Current Operation ◽  
Adaptive Extended Kalman Filter ◽  
Non Gaussian ◽  
Operation Status

Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The Kalman filter is an outstanding method for the SE, and the noise in the system is generally assumed to be Gaussian noise. In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice. Furthermore, it is hard to get the statistics of the state noise and measurement noise. As a result, a novel adaptive extended Kalman filter with correntropy loss is proposed and applied for power system SE in this paper. Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers. In addition, an adaptive update mechanism of the covariance matrixes of the measurement and process noises is introduced into the EKF with correntropy loss to enhance the accuracy of the algorithm. Extensive simulations are carried out on IEEE 14-bus and IEEE 30-bus test systems to verify the feasibility and robustness of the proposed algorithm.

Modified Re-Iterated Kalman Filter for Handling Delayed and Lost Measurements in Power System State Estimation

2020 ◽  
Vol 20 (7) ◽  
pp. 3946-3955 ◽  
Author(s):  
Imran Mohammed ◽  
Sreenath Jayakumar Geetha ◽  
Swapnil Sadashiv Shinde ◽  
Ketan Rajawat ◽  
Saikat Chakrabarti
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
State Estimation ◽  
System State ◽  
Power System State Estimation

Power System State Estimation: Detection of Topological Errors

1981 ◽  
Vol PER-1 (1) ◽  
pp. 19-19
Author(s):  
R. L. Lugtu ◽  
D. F. Hackett ◽  
K. C. Liu ◽  
D. D. Might
Keyword(s):  
Power System ◽  
State Estimation ◽  
System State ◽  
Power System State Estimation ◽  
Topological Errors
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