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With the proposal of the carbon peak goal, a multi-terminal flexible DC grid containing various renewable energy becomes the one of main ways of renewable energy power transmission. Thus, it is increasingly important to improve its transmission reliability and stability. This paper proposes a single-ended protection principle based on the analysis method of transient power change for the “mesh structure” ring-shaped flexible direct current (DC) grid. Based on the propagation characteristics of voltage, current, and anti-traveling waves during fault periods, the transient power variation coefficient based on SOD transformation is introduced to distinguish the internal and external faults. Besides, simulation based on the PSCAD/EMTDC platform within the Zhangbei four-port network verifies the effectiveness of the proposed protection principle under various fault conditions. Meanwhile, the protection criterion is designed according to the difference in the change coefficient of the transient power SOD within and outside the fault time zone. Moreover, the setting value of the start of protection is obtained by analyzing the simulation results, thus a set of fast and reliable single-ended protections for the same-side short-time transient power window of an enhanced flexible straight power grid is developed via combining it with the starting criterion of a sudden voltage change.

Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT) signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs). BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated.