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To address the sparse system identification problem in a non-Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive-inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performance of the sparsity aware maximum correntropy criterion algorithms by achieving a robust steady-state error. Considering an unknown sparse system, the l 1-norm and correntropy induced metric are employed in the RGMCC algorithm to exploit sparsity as well as to mitigate impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady-state estimation performance. [ABSTRACT FROM AUTHOR]

Kalman filters (KFs) are widely used for state-of-charge (SOC) estimation of Li-ion batteries due to their excellent dynamic tracking capability. Especially the cubature KF (CKF), with the computational efficiency and nonlinear processing ability, is an outstanding candidate for SOC estimation. However, the actual working conditions are complex and changeable, and the measurement data is usually accompanied by non-Gaussian noise (outliers). Therefore, the performance of the original CKF with minimum mean square error (MMSE) criterion may be degraded seriously in these cases. In order to enhance the robustness of CKF, the MMSE in the CKF framework is substituted by the generalized maximum correntropy criterion (GMCC), and thus a robust CKF with GMCC (GMCC-CKF) is developed by fixed point iteration approach in this work. Furthermore, a SOC estimation model via the GMCC-CKF is proposed to improve estimation accuracy under non-Gaussian noise environments. The simulation results show that, compared with the traditional KFs, the proposed GMCC-CKF can accurately estimate the SOC of lithium batteries under different temperatures and operating conditions considering non-Gaussian noise interference. The results of mean absolute error (MAE) and root mean square error (RMSE) are less than 1%, which verifies the excellent performance of GMCC-CKF. • GMCC is introduced into the CKF framework to develop a novel robust CKF. • The proposed GMCC-CKF method is applied for robust SOC estimation under non-Gaussian noise interferences. • Some simulations under different conditions are performed to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Ø The KRSL based adaptive filtering algorithm with sinusoidal signal is derived; Ø A recursive MKRSL based harmonic estimation method is developed; Ø A RMKRSL with adaptive gain factor is proposed to enhance the tracking ability. The development of effective harmonics estimation method plays a crucial role for safe operation of power system. Recently several existing parametric estimation approaches, however, are mainly developed and discussed under the assumption of Gaussian noise which is always unsuitable in real power systems. This work is mainly concerned with the robust parametric estimation methods. The kernel risk sensitive loss (KRSL) shows an advantage in that its performance surface is flat around the optimal solution while sharp far away from the optimal solution and it may exhibit outstanding performance in terms of faster convergence rate and higher estimation accuracy under non-Gaussian noise cases. Therefore, a minimum KRSL (MKRSL) algorithm with sinusoidal signal is designed, and we further develop a novel recursive MKRSL (RMKRSL) algorithm for harmonics estimation in non-Gaussian noise cases. In addition, an adaptive gain factor is introduced into the gain matrix of RMKRSL algorithm to enhance its tracking capability. Some simulations by using the signal generated from the given models and IEEE-1159-PQE databases are conducted to illustrate the performance of the proposed methods under different conditions. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions. [ABSTRACT FROM AUTHOR]

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. • The KRSL-EKF algorithm improves the estimation accuracy of FASE. • The AKRSL-EKF algorithm can effectively track the change of system state. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) plays a crucial role in battery management system, which directly reflects the usage of the battery. Recently the extreme learning machine (ELM) model as a data-driven method has been utilized to estimate SOC due to its simple single hidden layer structure and fast learning performance. The battery management system, however, may usually work in complex working conditions, which means that the non-Gaussian complex noise (or outliers) interference problem may exist in some measurement data for model training. So the performance of the classical ELM with mean square error (MSE) criterion may be degraded under this case. This work considers Non-Gaussian noise interference issue, the MSE in ELM is substituted by mixture generalized maximum correntropy criterion (MGMCC), and a novel robust ELM model is developed to improve the SOC estimation capability which mainly relies on the stable and robust nonlinear similarity characteristics of the MGMCC. A data set from a Panasonic 18,650 battery cell is used to verify the robustness of the proposed model, the experiment results demonstrate that it can achieve better estimation performance in terms of different evaluation metrics compared with the traditional methods. • A mixture GMCC is defined which can be as a learning criterion for NN model. • A robust ELM with MGMCC is developed to improve robust performance of the traditional ELM. • The MMGCCELM based SOC estimation approach is proposed to improve estimation accuracy under non-Gaussian noise interferences. [ABSTRACT FROM AUTHOR]