The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

When electric multiple units (EMU) pass the neutral zone, the traction transformer may generate sympathetic inrush, which will cause a malfunction in the transformer differential protection. In order to study the mechanism of the sympathetic inrush of the cascaded traction transformer, the flux–current model of the transformer, line impedance, power system voltage source, and other loads was established. On the basis of the flux–current circuit model, the influence of different factors on the sympathetic inrush of the traction transformer was analyzed. The analysis results were verified by simulation. Research results show that the remanence, closing angle, line impedance, and load will affect the duration and amplitude of the sympathetic inrush.

Wind shear is among the important sources of torque fluctuation on mechanical side and output power fluctuation on electromagnetic side and grid connection point, which frequency are triple turbine rotation frequency. With the increase in wind turbine single capacity and wind power integration scale, the power fluctuation caused by wind shear cannot be ignored. The paper takes a direct-drive permanent magnet synchronous wind turbine (PMSWT) as the research object. Firstly, the fluctuation transfer mechanism model is established; then, the current ripple is extracted by a notch filter to calculate the voltage control signal to suppress power fluctuation actively by the DC link capacitor which absorbs fluctuation energy. The simulation experiment shows that the suppression strategy is effective and reliable.

Setting reasonable circuit parameters is an important way to improve the quality of inverters, including waveform quality and power loss. In this paper, a circuit system of line voltage cascaded quasi-Z-source inverter (LVC-qZSI) is built. On this basis, the double frequency voltage ripple ratio and power loss ratio are selected as optimization targets to establish a multi-objective optimization model of LVC-qZSI parameters. To simplify the calculation, an integration optimization strategy of LVC-qZSI parameters based on GRA-FA is proposed. Where, the grey relation analysis (GRA) is used to simplify the multi-objective optimization model. In GRA, the main influence factors are selected as optimization variables by considering the preference coefficient. Then, firefly algorithm (FA) is used to obtain the optimal solution of the multi-objective optimization model. In FA, the weights of objective functions are assigned based on the principle of information entropy. The analysis results are verified by simulation. Research results indicate that the optimization strategy can effectively reduce the double frequency voltage ripple ratio and power loss ratio. Therefore, the strategy proposed in this paper has a superior ability to optimize the parameters of LVC-qZSI, which is of great significance to the initial values setting.