With the application of communication network technology in power systems, traditional power grids have gradually developed into cyber-physical systems (CPS), and false data injection (FDI) attacks have become a hidden danger that affects the operation of power CPS. To solve the problems of low estimation accuracy and model uncertainty when the extended Kalman filter (EKF) algorithm is subjected to FDI attacks in power systems, an interpolation adaptive H_∞ extended Kalman filter (IAHEKF) algorithm is proposed for the dynamic state estimation of power systems under FDI attacks. The new algorithm uses an interpolation strategy to reduce the linearization error of the EKF algorithm and introduces adaptive H_∞ theory to update the error covariance, minimizing the error upper bound caused by model uncertainty; moreover, it uses the Sage-Husa estimator to calculate the noise covariance, reducing the impact of unknown noise on state estimation. Finally, tests are conducted on the IEEE-14 node system and IEEE-30 node system, and the results show that the IAHEKF algorithm has higher estimation accuracy under different attack scenarios.

The existing State of Charge (SOC) estimation methods assume constant parameters for the battery model and do not consider the dynamic changes in these parameters, resulting in imprecise SOC estimates. In view of this limitation, the paper introduced an algorithm that combines online identification of battery model parameters with SOC estimation. Based on the second-order RC equivalent circuit model, it used Multi Innovation Least Squares (MILS) algorithm to identify the parameters in the lithium-ion battery model online, so as to modify the battery model in real time. At the same time, based on the modified battery model, it estimated the battery state of charge through Multi Innovation Extended Kalman Filter (MIEKF) algorithm. MILS algorithm can solve the problem of initial error accumulation in the process of online parameter identification, and can realize online accurate identification of model parameters. MIEK algorithm combines multi-innovation theory and Kalman filter theory, adds forgetting factor to weaken historical data and correct weight, solves the problem of data oversaturation, and has high accuracy and convergence. The experimental results show that, when identifying the parameters of the battery model, the Root Mean Square Error (RMSE) of the MILS algorithm is 1.4 mV, the RMSE of the RLS algorithm is 1.9 mV, and the estimation accuracy is improved by 26.3%. For the SOC estimation after parameter identification, the RMSE estimated by the MIEKF algorithm is 0.0037, while the RMSE estimated by the EKF and AEKF algorithms are 0.0073 and 0.0052, respectively. The MIEKF algorithm improves the estimation accuracy by 49.31% compared to the EKF algorithm and by 28.84% compared to the AEKF algorithm.Moreover, in the case of an incorrect initial SOC value, the proposed algorithm can converge to the true value after about 30 seconds of battery operation. The algorithm proposed in the paper is an effective estimation method with high accuracy and good robustness.

To improve the accuracy and stability of battery remaining useful life (RUL) prediction for lithium-ion batteries, this paper proposes a new convolutional neural network-gated recurrent unit-particle filter (CNN-GRU-PF) fusion prediction model. First, the battery capacity series is decomposed and reconstructed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and Pearson correlation coefficient method, which reduces the influence of noise on RUL prediction. Then, the capacity is predicted by CNN-GRU, and the CNN-GRU prediction value is used as the observation value of PF, and the prediction error of CNN-GRU is corrected by the state prediction ability of PF. A moving window is used to iteratively update the training set, and the PF optimization value is added to the CNN-GRU training set, forming an iterative training and dynamic updating between them, which improves the long-term prediction performance of CNN-GRU. To verify the effectiveness of proposed method, CNN-GRU-PF model is applied to predict the battery's RUL. The experiments show that CNN-GRU-PF improves the prediction accuracy of battery B5 by 87.27%, 82.88%, and 55.43% respectively compared with GRU, PF and GRU-PF, and also achieves significant improvement for other batteries. The new model is an effective RUL prediction method with good accuracy and robustness.