LIBSVM在铅酸蓄电池寿命预测中的应用研究
Application of LIBSVM in Life Prediction of Lead-Acid Battery
DOI: 10.12677/SG.2017.75045, PDF, HTML, XML, 下载: 1,611  浏览: 2,951 
作者: 杨传凯*：国网陕西省电力公司电力科学研究院，陕西 西安;西安电子科技大学微电子学院，陕西 西安；周际城：武汉大学电气工程学院，湖北 武汉；刘 伟, 李 旭：国网陕西省电力公司，陕西 西安；李良书, 付 峰：国网陕西省电力公司渭南供电公司，陕西 渭南；陈 凯：国网电力科学研究院武汉南瑞有限责任公司，湖北 武汉
关键词: LIBSVM；铅酸蓄电池；寿命预测；变电站；支持向量机；LIBSVM； Lead-Acid Battery； Life Prediction； Substation； Support Vector Machine
摘要: 铅酸蓄电池作为变电站直流电源系统的核心设备，随着投运时间的增长，将会发生内阻增大、容量减小问题，从而导致铅酸蓄电池组的使用寿命减小。因此，研究铅酸蓄电池的寿命预测方法，对于保障变电站的安全稳定运行具有重要作用。在介绍支持向量机的基本原理的基础上，结合铅酸蓄电池的样本数据，通过交叉验证选取LIBSVM回归机最优参数，通过支持向量机回归预测模型建立铅酸蓄电池的寿命预测模型。实验结果表明，基于LIBSVM的铅酸蓄电池寿命预测模型具有较高的预测精度，该方法是切实可行的。
Abstract: As the core of the DC power supply system, the performance of the lead-acid battery is the safe and stable operation of the whole substation. With the use of lead-acid battery pack time increases, the battery’s internal resistance will increase, the battery capacity will be reduced, resulting in lead-acid battery life. Therefore, it is of great significance to study the life prediction of lead-acid battery. Based on the basic principle of support vector machine (SVM) and the sample data of lead-acid battery, the optimal parameters of LIBSVM regression machine are selected by cross validation, and the life prediction model of lead-acid battery is established by support vector machine regression model. The experimental results show that the LIBSVM-based lead-acid battery life prediction model has high prediction accuracy, and the method is feasible.
文章引用：杨传凯, 周际城, 刘伟, 李旭, 李良书, 付峰, 陈凯. LIBSVM在铅酸蓄电池寿命预测中的应用研究[J]. 智能电网, 2017, 7(5): 412-419. https://doi.org/10.12677/SG.2017.75045

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