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Energy storage battery soft fault diagnosis method based on GA-Elman

A diagnostic method and energy storage battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of difficult diagnosis of soft faults in large-capacity battery energy storage systems, and achieve high accuracy results

Inactive Publication Date: 2017-09-22
NORTH CHINA ELECTRIC POWER UNIV (BAODING) +2
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Problems solved by technology

However, there are still problems in the application of soft fault diagnosis of large-capacity battery energy storage systems.

Method used

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  • Energy storage battery soft fault diagnosis method based on GA-Elman
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  • Energy storage battery soft fault diagnosis method based on GA-Elman

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Embodiment Construction

[0039] The invention proposes a GA-Elman-based soft fault diagnosis of an energy storage battery. The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0040] Such as figure 1 As shown, the GA-Elman-based energy storage battery soft fault diagnosis method of the present invention includes: according to the characteristics of the energy storage battery soft fault, combined with the fault signal feature extraction technology and fuzzy mathematics, the fuzzy division of the soft fault of the large-capacity battery energy storage system is realized .

[0041] Specifically include the following steps:

[0042] Step 1. Collect the terminal voltage signal and state-of-charge signal of each battery cell in the battery energy storage system, and transmit the collected signal to the computer for denoising processing;

[0043] Step 2, performing feature vector extraction on the processed signal in step 1, and performing normalization;

[0...

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Abstract

The invention belongs to the technical field of battery energy storage and especially relates to an energy storage battery soft fault diagnosis method based on GA-Elman. The method comprises the following steps: carrying out acquisition and analysis on terminal voltage signals and state-of-charge signals under different degrees of attenuation; obtaining two feature vectors of terminal voltage deviation Devk and SOC punishment angle [theta]k; carrying out normalization processing on the feature vectors and serving the processed feature vectors as input of the GA-Elman neural network, and serving residual capacity Qk obtained after battery attenuation as output of the GA-Elman neural network; calculating relative inferiority degree of the Qk; determining four fuzzy subsets and membership functions thereof in a large-capacity battery energy storage system soft fault discourse domain according to the relative inferiority degree; and establishing a soft fault diagnosis model having fuzzy output and realizing fuzzy diagnosis of soft fault of the battery energy storage system. The method has higher accuracy, has battery soft fault level comprehensive judgment capability and points out a new direction for soft fault diagnosis of a large-capacity battery storage system.

Description

technical field [0001] The invention belongs to the technical field of battery energy storage, and in particular relates to a GA-Elman-based soft fault diagnosis method for an energy storage battery. Background technique [0002] With the increasingly serious energy crisis and environmental problems, green and clean energy sources such as wind power and photovoltaics have attracted increasing attention. Wind power and photovoltaics are random, fluctuating and intermittent, and their large-scale grid connection brings great challenges to the safe, reliable and efficient operation of the system. Large-capacity battery energy storage systems help to improve the performance of large-scale intermittent renewable energy. Research on volatility and intermittency has become a hot spot. The energy storage battery is a key component of the large-capacity battery energy storage system, and it is also the main source of failure of the battery energy storage system. The research on the ...

Claims

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Application Information

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IPC IPC(8): G01R31/36G06N3/04
CPCG01R31/367G06N3/048G06N3/044
Inventor 韩晓娟徐寿臣王春玲
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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