蓄电池放电设备故障自动化测试方法优化

Automatic detection method of battery leakage fault based on attribute reduction

蓄电池在输电配电过程中受到电磁场干扰以及蓄电池电源组件之间的串扰影响,容易产生蓄电池漏电故障,为了提高蓄电池的漏电故障诊断效率,提出一种基于属性约简的蓄电池漏电故障自动检测方法。采用电源传感信息特征采样方法进行蓄电池的属性信息采集,提取蓄电池电源分布式组件中的漏电信号,对蓄电池的漏电信号进行高阶谱特征分解和属性约简挖掘,采用谱分析模型进行蓄电池传输信号的特征提取,根据提取的属性约简特征量的异常性进行蓄电池的漏电故障分析,采用自适应学习算法进行蓄电池漏电的模式识别和自动诊断,提高蓄电池漏电故障检测的自适应性和全局收敛性。仿真结果表明,采用该方法进行蓄电池漏电故障检测的智能性较好,检测准确性较高,提高了故障诊断的自适应性和自动化水平。

In order to improve the efficiency of battery leakage diagnosis,the battery is easily affected by electromagnetic field interference and crosstalk between battery power components during transmission and distribution.An automatic fault detection method for battery leakage based on attribute reduction is proposed.The characteristic sampling method of power sensor information is used to collect the attribute information of the battery,and the leakage signal is extracted from the distributed module of the battery power supply,and the high order spectrum characteristic decomposition and attribute reduction mining of the leakage signal of the battery are carried out.The spectral analysis model is used to extract the characteristics of the transmission signal of the battery,and the leakage fault of the battery is analyzed according to the anomaly of the extracted attribute reduction characteristic quantity.The adaptive learning algorithm is used to identify and diagnose the leakage of battery,which improves the self-adaptability and global convergence of the fault detection of battery leakage.The simulation results show that this method is more intelligent and accurate in detecting battery leakage fault,and improves the self-adaptability and automation level of fault diagnosis.