Zhang Zhiyu, Qiu Jinzhe, Ma Wentao
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Entropy (Basel). 2019 Mar 18;21(3):293. doi: 10.3390/e21030293.
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.
监测电力系统当前的运行状态对于提升电网以满足未来需求起着至关重要的作用。因此，电力系统的实时状态估计（SE）一直备受广泛关注。卡尔曼滤波器是用于状态估计的一种出色方法，并且通常假定系统中的噪声为高斯噪声。然而，在实际电力系统中，这些测量值在实践中通常会受到非高斯噪声的干扰。此外，很难获取状态噪声和测量噪声的统计信息。因此，本文提出了一种具有核相关熵损失的新型自适应扩展卡尔曼滤波器，并将其应用于电力系统状态估计。首先，核相关熵用于在存在非高斯噪声和异常值的情况下提高扩展卡尔曼滤波算法的鲁棒性。此外，将测量噪声和过程噪声协方差矩阵的自适应更新机制引入到具有核相关熵损失的扩展卡尔曼滤波器中，以提高算法的准确性。在IEEE 14节点和IEEE 30节点测试系统上进行了大量仿真，以验证所提算法的可行性和鲁棒性。
