A microgrid is a typical cyber-physical system. The coordinated control of each unit in a microgrid mainly relies on the cyber system. Once performance degradation of the cyber system happens, such as outages or transmission delay, the stable operation of the microgrid will likely be affected. In this paper, an analytical method is presented to quantify the impacts on the microgrid operation reliability from cyber system element failures and transmission interference. First, the link model and the information transmission model of the cyber system are established to describe the cyber routing path and the information transmission service quality, respectively. Then, the sequential Monte Carlo method is employed to simulate the operation of the microgrid. Finally, the impacts of the element failure and the transmission interference of the cyber system on microgrid operational reliability is quantitatively analyzed. Additionally, sensitivity analysis is carried out, focusing on the impacts of the cyber network topology, element failure rate and different types of information transmission quality indexes including delays, routing error, and information transmission error. The results of the numerical examples demonstrate the effectiveness of the proposed model and method.

A fast reliability calculation method based on the fault incidence matrix (FIM) for the complex distribution system is proposed in this paper. First, the M-segment-N-tie-switch (MSNT) reliability calculation unit for the complex distribution network is established and the power supply path matrix is obtained by inversing the node branch incidence matrix of the MSNT unit. Then, three types of FIMs are constructed considering the influence type of each fault event on the load points. Finally, the system reliability indexes are calculated through the algebraic operation between the FIMs and the fault event parameter vectors. The accuracy and the efficiency of this method are verified using the IEEE RBTS bus-6 system. The reliability evaluation method proposed in this paper avoids complicated fault event enumeration and repetitive fault influence range searching, thus, saves the computation time greatly. And more importantly, the analytical expression of the reliability indexes in the matrix form can clearly show the influence of each fault event on each load interruption. So, it is convenient for the reliability sensitivity analysis and helpful for the operator with the reliability improvement planning.

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0-1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network.

To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the RMSE and prediction time of the proposed framework are 1.078% and 15.728 s under the total HIs. The performance of the proposed framework under a different number of HIs is also analyzed. The experimental results show that the proposed framework can achieve the optimal prediction accuracy (98.978%) under the HIs of resistances, loss modes, and isobaric voltage drop time.