Noise reduction method for medium-voltage uninterruptible power supply device
A power-off device and medium-voltage technology, which is applied in the field of noise reduction, can solve the problems of low noise data accuracy and low data retrieval efficiency, and achieve the effect of ensuring integrity and improving data retrieval efficiency
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[0035] Example 1
[0036] see figure 1 As shown, the present invention is a noise reduction method for a medium voltage uninterruptible power supply device, and this embodiment specifically discloses a classification filtering method:
[0037] Install monitoring equipment and detect synchronization rate: The staff installs the noise monitoring equipment on the uninterruptible power supply device, supplies power to it through the UPS, and adjusts the transmission synchronization of each group of noise monitoring equipment.
[0038] It needs to be further explained that the UPS is specifically an uninterruptible power supply including an energy storage device, which is mainly used to provide uninterrupted power supply to some devices that require high power supply stability.
[0039] Collect and classify the UPS information of each group: collect the relevant data of the UPS in each area, build a classification repository based on the MySql database, and import each group of da...
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[0042] Example 2
[0043] see figure 1 As shown, the present invention is a noise reduction method for a medium-voltage uninterruptible power supply. Except for the same structure as the above-mentioned embodiment, this embodiment specifically discloses an analysis and adjustment method:
[0044] Collect the noise data of each group of devices for analysis: The server collects the noise data of the running UPS in real time through the noise monitoring equipment, and at the same time analyzes and adjusts the collected noise data of each group.
[0045] Specifically, first, the server builds an optimized learning model, and imports each group of noise data collected by the noise monitoring device into the optimized learning model. The entire data value is divided according to user-defined rules, and the divided data is integrated to generate a noise set S n ={A 1 , A 2 , A 3 ,...,A x }, where n is the total number of noise sets divided, x is the total number of noise data ...
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