摘要
为确保通信电源系统的可靠性,及时掌握蓄电池的健康状态,采用BP神经网络模式识别方法,使用内阻、浮充电压、复升最高电压和温度这4个能最大程度影响蓄电池健康状态的性能参数构建蓄电池健康状态评价模型。仿真结果表明:经过学习训练的网络模型可以有效评估蓄电池的运行性能和健康状况,其正确判断率可达99.2%。
In order to guarantee the reliability of communication power supply system and grasp the health status of storage battery,BP neutral network pattern identification method is adopted,and the health status evaluation model of storage battery is built by using four performance parameters including internal resistance,floating charge voltage,maximum restoration voltage and temperature which can affect the health status of storage battery at maximum degree.Simulation results show that the neural network after training can effectively evaluate the operation performance and health status of storage battery whose correct judgment rate is up to 99.2%.
作者
胡秀园
陆贻名
莫飘
HU Xiuyuan;LU Yiming;MO Piao(Baise Power Supply Bureau,Guangxi Power Grid Co.,Ltd.,Guangxi Baise 533000,China)
出处
《广西电力》
2018年第2期44-47,58,共5页
Guangxi Electric Power
关键词
电力通信
蓄电池
BP神经网络
状态评价
electric power communication
storage battery
BP neutral network
state evaluation
