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In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.

The quantitative study of earthquake-triggered rockfall debris along seismogenic fault zones has proven to be a valid approach for use in identifying paleo-earthquakes and faulting activities. After the 2010 Yushu earthquake, paleo-rockfall research employed U-series dating of calcareous coats on the rockfall surface, and the results provided chronological evidence of multi-period features relating to earthquake-triggered rockfalls that have occurred along the Yushu fault. In this study, the mechanism involved in forming the calcareous coat on seismic rockfall surfaces was analyzed to further explain the significance of conducting associated U–Th aging analyses. For earthquake-triggered rockfall regions, the U–Th age of the calcareous coat can be used as a proxy age of the rockfall formation, and the timing of the related paleo-earthquake event can be constrained. Based on all current existing age data, an in-depth comparison was made between the U–Th ages of calcareous coats obtained from five seismic rockfall sites and other 14C dating results obtained from trenches and paleo-seismic landslides relating to paleo-earthquakes. Combined with the results of field investigations, four individual paleo-earthquakes that triggered rockfalls of different scales in historical time were identified. These results imply that multi-period earthquake-triggered rockfalls along the Yushu fault may be valid time indicators of seismic events and reflect the intensity and meizoseismal scale of relevant paleo-earthquake events. This exploratory research on the U-series dating of calcareous coats from earthquake-triggered rockfalls in Yushu can be used as a valuable reference for paleo-earthquake studies in other tectonically active regions.

A large variety of high-value substation relay protection equipment occupies a considerable amount of inventory space and capital in electric power companies. To improve this problem, this study proposes an inventory prediction model based on the remaining useful life (RUL) of equipment. The model acquires the RUL data of equipment by using the support vector regression (SVR) algorithm, and then, by taking this data as the main factor and the environmental factors and human factors during the operation of equipment as secondary factors, the model can realize the prediction of relay protection equipment in the substation. At the same time, the nature of the enterprise and the requirements for safety inventory are considered. The comparison of calculation results and error analysis, as well as the calculation time, all indicate that the RUL-based inventory forecasting is the best one. This model not only has high prediction accuracy but also has strong stability and portability. The model can provide a strong decision basis for improving the inventory management of the enterprise, enhancing the resource allocation capability, and formulating the spare parts procurement plan under the condition that the spare parts inventory reaches the safety stock.

The increasing integration of renewable energy sources has brought about a great computational burden for the traditional methods that assess the power system reliability. To reduce the computational costs, an extendable Latin hypercube importance sampling (ELHIS) method that combines importance sampling (IS) and Latin hypercube sampling (LHS) is proposed in this paper. First, the challenge of combining IS and LHS is analysed, and a customized sampling process of LHS is designed accordingly. Then, in the IS part of ELHIS, the cross entropy theory and Gaussian mixture model are adopted in the stage that constructs the quasi-optimal probability distribution of random variables. In addition, the samples in this stage are also utilized in the estimation of reliability indices to reduce the waste of computational efforts. In the LHS part, an extendable LHS approach is used to make ELHIS adaptive and flexible in determining the sample size to reach the required accuracy. Finally, numerical tests are performed on the modified IEEE-RTS 79 test system, where the real historical data from three wind farms and a photovoltaic station in Northwest China are employed. The results show that ELHIS is obviously faster than recent IS methods when used to assess the power system reliability.