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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Применить Всего найдено 2. Отображено 2.
23-06-2010 дата публикации

Electrified railroad homo-phase traction power supply system

Номер: CN0101746283A
Принадлежит:

The invention discloses an electrified railroad homo-phase traction power supply system. A traction transformer (SS1) of a traction substation is a three-phase-two-phase transformer; three phases (A, B and C) of the primary side of the traction transformer (SS1) are connected with a public power grid; a voltage reduction transformer (G), a first single-phase current transformer (M1), a direct-current capacitor (C) and a second single-phase current transformer (M2) are connected between an alpha output phase and a beta output phase of the secondary side in turn; the alpha phase supplies power to a locomotive (L), and the output voltage of the alpha phase is 27.5kV; and the output voltage of the beta phase is between 600 and 2,000V. The system can implement two-arm homo-phase power supply of the railroad traction substation without split phase, reduces the split phase link, is favorable for high-speed, stable and safe operation of the railroad, and has the advantages of simple structure, ...

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04-06-2014 дата публикации

Artificial intelligence identification method of external faults of conversion current bus of HVDC system

Номер: CN103837784A
Принадлежит:

The invention provides an artificial intelligence identification method of external faults of a conversion current bus of an HVDC system. The artificial intelligence identification method is characterized in that a wavelet-transform multi-scale decomposition algorithm is utilized to decompose system fault signals into different frequency sections, different wavelet energy skewnesses of different frequency-section signals are used as fault diagnosis feature vectors, and the fault diagnosis feature vectors are combined with a BP neural network to achieve accurate identification of fault types of an AC system. Compared with an AC system fault type identification method in the prior art, the method only needs to extract characteristics of a fault signal from the aspect of energy distribution, is not affected by the system operation mode, and is simple in operation, good in accuracy, and high in reliability.

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