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Storage battery fault diagnosis method and device based on fault injection deep learning

A fault diagnosis device and deep learning technology, applied in the direction of measurement device, measurement of electricity, measurement of electric variables, etc., can solve the problem of low battery detection efficiency, and achieve the effect of improving diagnosis efficiency, ensuring safety, and reducing workload

Pending Publication Date: 2021-06-11
GUANGDONG POWER GRID CO LTD +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, each nuclear capacity requires operation and maintenance personnel to work continuously on site for a long time, and it is necessary to carry an additional battery pack to replace the battery pack to be tested before testing, and the battery detection efficiency is extremely low

Method used

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  • Storage battery fault diagnosis method and device based on fault injection deep learning
  • Storage battery fault diagnosis method and device based on fault injection deep learning
  • Storage battery fault diagnosis method and device based on fault injection deep learning

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Embodiment Construction

[0040] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings but not all structures.

[0041] figure 1 A flow chart of a battery fault diagnosis method based on fault injection deep learning provided by an embodiment of the present invention, as shown in figure 1 As shown, the battery fault diagnosis method based on fault injection deep learning provided in this embodiment includes:

[0042] Step S101, connecting each battery in the battery pack to be tested to the battery fault diagnosis device.

[0043] The battery fault diagnosis method based on fault injection deep learning provided in this em...

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Abstract

The invention discloses a storage battery fault diagnosis method and device based on fault injection deep learning, and the method comprises the steps: enabling each storage battery in a to-be-detected storage battery pack to be connected with a storage battery fault diagnosis device, then, adopting the storage battery fault diagnosis device to detect battery performance parameters of each storage battery in the to-be-detected storage battery pack in the charging and discharging process, and finally, inputting the battery performance parameters of each storage battery in the charging and discharging process into the deep learning model based on fault injection; and determining whether each storage battery has a fault or not and the fault type of the storage battery with the fault, wherein the deep learning model based on fault injection is generated after deep learning is carried out on the training storage battery pack. According to the storage battery fault diagnosis method and device based on fault injection deep learning disclosed by the embodiment of the invention, the diagnosis efficiency of the storage battery of the power distribution network can be improved.

Description

technical field [0001] The electric power technology of the embodiment of the present invention particularly relates to a battery fault diagnosis method and device based on fault injection deep learning. Background technique [0002] Due to the small size, light weight, high discharge performance, safety and reliability, and low maintenance of the battery, the battery is often configured as a backup power source in the distribution network area. At present, the battery is generally used in the communication power supply of the distribution network area. Theoretically, batteries have high reliability and long service life. However, due to the lack of effective on-line diagnostic methods, many battery packs are far from reaching the rated service life in actual use, and the problem of insufficient power supply capacity often occurs. In fact, after 2-3 years of use, most of the batteries are difficult to pass the capacity test, and even some single batteries fail after one or t...

Claims

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Application Information

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IPC IPC(8): G01R31/388G01R31/3842G01R31/367G01D21/02
CPCG01R31/367G01R31/3842G01R31/388G01D21/02
Inventor 李通李顺尧万四维薛峰陈世昌郑风雷苏华锋
Owner GUANGDONG POWER GRID CO LTD
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