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The reproducible fabrication of large-area zeolite membranes for gas separation is still a great challenge. We report the scalable fabrication of high-performance zeolite MFI membranes by single-step secondary growth on the 19-channel alumina monoliths for the first time. The packing density and mechanical strength of the monolithic membranes are much higher for these than for tubular ones. Separation performance of the monolithic membranes toward the butane isomer mixture was comparably evaluated using the vacuum and Wicke–Kallenbach modes. The n-butane permeances and n-butane/i-butane separation factors for the three membranes with an effective area of ∼84 cm2 were >1.0 × 10–7 mol (m2 s Pa)−1 and >50 at 343 K for an equimolar n-butane/i-butane mixture, respectively. We succeeded in scaling up the membrane synthesis with the largest area of 270 cm2 to date which has 1.3 times the area of an industrial 1 m long tubular membrane. Monolith supported zeolite MFI membranes show great potential for industrial n-butane/i-butane separation.

Owing to the narrow transmission line corridors, double-circuit HVDC transmission line on the same tower has been put into engineering construction. Based on the lines transposed fully, the characteristics of fault travelling-wave in double-circuit and single-circuit HVDC is compared firstly. And then referring to the actual calculation method of criterion for travelling-wave protection, the changes of setting value in double-circuit on contrast of single-circuit are discussed. Finally, the operating ability of two travelling-wave protection schemes in double-circuit HVDC is contrastively discussed.

High voltage direct current (HVDC) transmission systems play a critical role to optimize resource allocation and stabilize power grid operation in the current power grid thanks to their asynchronous networking and large transmission capacity. To ensure the operation reliability of the power grid and reduce the outage time, it is imperative to realize fault diagnosis of HVDC transmission systems in a short time. Based on the prior research on fault diagnosis methods of HVDC systems, this work comprehensively summarizes and analyzes the existing fault diagnosis methods from three different angles: fault type, fault influence, and fault diagnosis. Meanwhile, with the construction of the digital power grid system, the type, quantity, and complexity of power equipment have considerably increased, thus, traditional fault diagnosis methods can basically no longer meet the development needs of the new power system. Artificial intelligence (AI) techniques can effectively simplify solutions’ complexity and enhance self-learning ability, which are ideal tools to solve this problem. Therefore, this work develops a knowledge graph technology-based fault diagnosis framework for HVDC transmission systems to remedy the aforementioned drawbacks, in which the detailed principle and mechanism are introduced, as well as its technical framework for intelligent fault diagnosis decision.