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Multi-layer SVM (support vector machine) based storage battery on-line monitoring method

A support vector machine and storage battery technology, which is applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as insufficient judgment accuracy and battery performance degradation

Inactive Publication Date: 2012-10-24
李昌
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the defect that the judgment accuracy of the existing battery performance degradation fault diagnosis technology is not high enough, and propose a battery online monitoring method based on a multi-layer support vector machine with a higher diagnosis accuracy

Method used

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  • Multi-layer SVM (support vector machine) based storage battery on-line monitoring method
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  • Multi-layer SVM (support vector machine) based storage battery on-line monitoring method

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Embodiment Construction

[0042] In this embodiment, the data records of the 480-section Narada power supply with a model of "GFM-1000E" that have been guaranteed for more than 4 years are classified and decided one by one. The structure diagram of the monitoring decision-making process is as follows: figure 1 As shown, firstly, the SVM data classification training set is established. The method is to conduct an offline checking discharge test on each battery selected as the training set sample, and according to the measured battery discharge capacity, the remaining capacity (percentage) after floating charge The size is marked, and the identification is carried out according to the following formula:

[0043] According to the actual measurement, the empirical value is 3~5;

[0044] In this specific example, there are 100 healthy batteries (identified value 1) in the sample set, 100 sub-healthy batteries (identified value 0.5), and 50 deteriorated batteries (identified value 0). The sampling time ...

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Abstract

The invention relates to a multi-layer SVM (support vector machine) based storage battery on-line monitoring method. Considering that the storage battery performance degradation process is closely related to the voltage and current variation condition and temperature variation condition, in order to reflect the intrinsic relationship, through setting up an SVM data category training set, and utilizing a multi-layer SVM storage battery performance degradation diagnosis flow by adopting an SVM algorithm, the storage battery is subjected to real-time intelligent fault predication, the monitoring of the health state of the storage battery has higher accuracy compared with the traditional method, and also early warning can be provided in case of the invalidity of the storage battery.

Description

technical field [0001] The present invention relates to a battery performance degradation diagnosis method, in particular to an online battery monitoring method based on a multi-layer support vector machine (SVM: Support Vector Machine), which belongs to the field of power supply fault diagnosis. Background technique [0002] A safe and stable power supply system is an important foundation for the development of the national economy and a necessary condition for the harmonious development of a modern industrial society. As a backup battery for substations, computer rooms, mobile base stations, and UPS power supply systems, VRLA (fully enclosed maintenance-free lead-acid batteries) have been widely used in power systems in communications, electric power, transportation, finance and other industries for real-time monitoring Can effectively ensure the reliability of the power supply system. [0003] VRLA is a complex electrochemical system. Battery electrode materials, proces...

Claims

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Application Information

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IPC IPC(8): G01R31/36G06K9/62
Inventor 李昌
Owner 李昌
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