The accurate estimation of the state of charge (SOC) for lithium-ion batteries’ performance prediction and durability evaluation is of paramount importance, which is significant to ensure reliability and stability for electric vehicles. The SOC estimation approaches based on big data collection and offline adjustment could result in imprecision for SOC estimation under various driving conditions at different temperatures. In the traditional GM(1,1), the initialization condition and the identifying parameter could not be changed as soon as they are confirmed. Aiming at the requirements of battery SOC estimation with non-linear characteristics of a dynamic battery system, the paper presents a method of battery state estimation based on Metabolic Even GM(1,1) to expand battery state data and introduce temperature factors in the estimation process to make SOC estimation more accurate. The latest information data used in the optimized rolling model is introduced through the data cycle updating. The experimental results show that the optimized MEGM(1,1) effectively considers the influence of initial data, and has higher accuracy than the traditional GM(1,1) model in the application of data expansion. Furthermore, it could effectively solve the problem of incomplete battery information and battery capacity fluctuation, and the dynamic performance is satisfactory to meet the requirements of fast convergence. The SOC estimation based on the presented strategy for power batteries at different temperatures could reach the goal of the overall error within 1% under CLTC conditions with well robustness and accuracy.

The superconducting magnetic energy storage (SMES) is with the features of quick system adjustment and transient stability enhancement for grid-connected voltage source converter (VSC). To suppress the resonant peak generated by LCL topology, the active damping method utilizing internal model is employed. But the active damping could result in noise amplification and the system delay, which will affect the damping effect. Thus the multi-loop feedback control in static coordinate system is established by using second-order generalized integral, and the negative band pass filter (NBPF) and the delay compensation with optimized Newton interpolation are presented to reduce the resonance peak. And the quasi-proportional resonant (QPR) regulator is adopted for inner current loop to realize the sinusoidal current without static error, and also the quasi-direct power control (QDPC) based on the instantaneous power model is employed in the outer current loop. Through simulation and experimental verification, the presented strategy in this paper significantly reduces the total harmonic distortion (THD) of line current, and realizes unity power factor. Additionally, it improves the power quality with well dynamic and static response characteristics.

The shield multiconductor cable has been widely utilized in smart power grid with the characteristics of compact structure, low loss, high current carrying capacity and less environmental pollution. To achieve the accurate estimation of shield multiconductor cable for electromagnetic application, the partial element equivalent circuit (PEEC) topology with high-frequency parasitic parameters is established, and the distributed parameter model with Γ topology is proposed to describe the dynamic transmission features. The skin and proximity effect of shield cable can affect current distribution density, and the trivial solution with insufficient model estimation accuracy will result in the fitting residuals. Thus the R-L ladder based on N -branch topology is analyzed, and the vector fitting iteration strategy achieves the accurate approximation and fast convergence for the estimation model. Considering the continual charging and discharging characteristics caused by high-frequency dv/dt and di/dt , Neumann Law and Mirror theory are employed to derive the partial capacitance of shield multiconductor cable model. Finally, the simulation and test results with the presented cable model for conducted EMI assessment are performed on the test bench, the reliability of the parameter estimation is experimentally verified and its effect on high-frequency conducted interference is accurately characterized.

The reliability analysis of low-voltage switchgear is of great importance to the safety of power system. Bayes theory is a widely used reliability evaluation method because it can make full use of historical data. Therefore, the reliable prior information is the key factor to ensure the correctness of the reliability analysis results when the Bayes method is used to evaluate the reliability of the low-voltage switchgear. In order to obtain the priori information of low-voltage switchgear with high reliability, a feasible prior information fusion method was proposed and a reliable prior distribution function was obtained. First, kinds of historical data and effective information were collected from manufacturers and users. Then, to get more accurate prior distribution, the parametric and nonparametric test method was used to test the compatibility of the collected historical data, the effective information and the field information. Finally, the prior distribution and the expert experience were fused, and the conjugate prior distribution of the reliable low-voltage switchgear was constructed, which provided the theoretical basis for improving the accuracy of reliability evaluation of the low-voltage switchgear.