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Robust forecasting-aided state estimation of power system based on extended Kalman filter with adaptive kernel risk-sensitive loss
International Journal of Electrical Power & Energy Systems ( IF 5.2 ) Pub Date : 2022-12-06 , DOI: 10.1016/j.ijepes.2022.108809
Tong Gao, Jiandong Duan, Jinzhe Qiu, Wentao Ma

State estimation (SE) plays a pivotal role in the development of modern power system. Accurate forecasting-aided state estimation (FASE) can track the sudden changes of power system state and maintain the safe operation of modern power system. However, the performance of existing FASE methods is affected by anomalies in real power system, such as sudden state changes and bad data. To address this problem, a robust algorithm based on extended Kalman filter (EKF) with the kernel risk-sensitive loss (KRSL) (called KRSL-EKF) is proposed for FASE. The KRSL-EKF, taking KRSL as the cost function of the original EKF algorithm, can overcome the limitations of the EKF to perform higher estimation accuracy under non-Gaussian noise cases. In addition, an adaptive method is further introduced into the proposed KRSL-EKF algorithm to adjust the covariance matrices of process noise and measurement noise, and we denote it as AKRSL-EKF. The novel AKRSL-EKF algorithm can effectively adapt the noise to system state variations and achieves better estimation accuracy. The effectiveness of the proposed algorithms for FASE is verified on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems. The results show that the estimation accuracy of the proposed algorithms is 30% higher than other traditional algorithms, with high estimation accuracy.



中文翻译:

基于自适应核风险敏感损失的扩展卡尔曼滤波器的电力系统鲁棒预测辅助状态估计

状态估计(SE)在现代电力系统的发展中起着举足轻重的作用。准确的预测辅助状态估计(FASE)可以跟踪电力系统状态的突然变化,维护现代电力系统的安全运行。然而,现有 FASE 方法的性能受到实际电力系统异常的影响,例如状态突然变化和不良数据。为了解决这个问题,针对 FASE 提出了一种基于扩展卡尔曼滤波器 (EKF) 和内核风险敏感损失 (KRSL) 的鲁棒算法(称为 KRSL-EKF)。KRSL-EKF将KRSL作为原EKF算法的代价函数,可以克服EKF的局限性,在非高斯噪声情况下实现更高的估计精度。此外,在所提出的 KRSL-EKF 算法中进一步引入自适应方法来调整过程噪声和测量噪声的协方差矩阵,我们将其表示为 AKRSL-EKF。新颖的AKRSL-EKF算法可以有效地使噪声适应系统状态变化并获得更好的估计精度。所提出的 FASE 算法的有效性在 IEEE 14 总线、IEEE 30 总线和 IEEE 57 总线系统上得到验证。结果表明,所提算法的估计精度比其他传统算法提高了30%,具有较高的估计精度。所提出的 FASE 算法的有效性在 IEEE 14 总线、IEEE 30 总线和 IEEE 57 总线系统上得到验证。结果表明,所提算法的估计精度比其他传统算法提高了30%,具有较高的估计精度。所提出的 FASE 算法的有效性在 IEEE 14 总线、IEEE 30 总线和 IEEE 57 总线系统上得到验证。结果表明,所提算法的估计精度比其他传统算法提高了30%,具有较高的估计精度。

更新日期:2022-12-06
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