基于模糊神经网络的蓄电池劣化程度预测研究

Impairment degree forecast for battery based on fuzzy neural network

阀控铅酸盐蓄电池是变电站通信电源系统的重要组成部分,担负着在故障状态下为变电站通信系统提供不间断供电电源的重任。通过对阀控铅酸盐蓄电池劣化程度的各种因素进行分析,研究了采用模糊神经网络建立阀控铅酸盐蓄电池劣化程度预测模型,通过对测量数据进行劣化程度的预测,与实际测量数据进行比较,证明预测模型具有较高的可靠性。

Valve regulated lead acid battery is a very important part of the transformer substation communication power supply system,which plays an important role in supplying uninterruptible power supply to transformer substation communication system when the other part of power fails.By analyzing the factors affecting the impairment degree of valve regulated lead acid battery,fuzzy neural network was used to build the impairment degree forecast model of valve regulated lead acid battery.By comparison of the forecast data and the real data,the forecast model was proved the validity.