As the key component of a power system, the power transformer directly impacts the reliability and safety of the system.

The selection of power transformer is very important to power sector. Most methods are utilized according to the initial cost and don't consider the synthetical evaluation of economy and technology. Based on previous research, this paper addresses a new practical probabilistic life cycle cost model. Then, in order to demonstrate the practicability of probabilistic life cycle cost for the power transformer, illustrative investment alternatives of actual power transformers are discussed. From the result of the numerical investigation, it may be positively stated that the optimum investment alternative for the power transformer based on the probabilistic life cycle cost model proposed in this study will lead to a more rational, economical and effective procedure compared with the conventional method only considering the initial cost.

Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

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A rock bolt refers to a reinforcing bar used commonly in geotechnical engineering. Also, defect identification of bolt anchorage system determines the safe operation of the reinforced structures. In the present paper, to accurately extract defect information, a CNN model based on time-frequency analysis is proposed, covering both time-domain and frequency-domain information. The effect of the number of convolution kernels on the defect identification results is discussed. By laboratory experiments, the performances of STFT-based CNN with those of time-domain input or frequency-domain input-based 1D CNN are compared, and the results demonstrate that the proposed method showed enhanced performance in identification accuracy.