Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation
Zhiyu Zhang1, Jinzhe Qiu1, Wentao Ma1
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a robust adaptive extended Kalman filter using correntropy loss for power system state estimation (SE). The novel method enhances accuracy and reliability, even with non-Gaussian noise, improving grid monitoring.
Area of Science:
- Electrical Engineering
- Control Systems
- Signal Processing
Background:
- Real-time power system state estimation (SE) is crucial for grid enhancement.
- Traditional Kalman filters assume Gaussian noise, which is often violated in practice.
- Non-Gaussian noise and unknown noise statistics challenge conventional SE methods.
Purpose of the Study:
- To develop a robust SE algorithm for power systems facing non-Gaussian noise.
- To improve the accuracy and reliability of state estimation under practical operating conditions.
- To address the limitations of standard Kalman filters in real-world power system applications.
Main Methods:
- A novel adaptive extended Kalman filter (EKF) incorporating correntropy loss is proposed.
- Correntropy is utilized to enhance the algorithm's robustness against non-Gaussian noise and outliers.
- An adaptive mechanism for updating measurement and process noise covariance matrices is integrated.
Main Results:
- The proposed EKF with correntropy loss demonstrated improved robustness in simulations.
- The adaptive covariance update mechanism enhanced the accuracy of the state estimation.
- Feasibility and robustness were verified on IEEE 14-bus and IEEE 30-bus test systems.
Conclusions:
- The novel adaptive EKF with correntropy loss is effective for power system SE.
- The method offers a robust solution for handling non-Gaussian noise in power grids.
- This approach enhances the reliability of real-time grid monitoring and control.
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