基于神经网络的蓄电池容量预测研究
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摘要
针对当前变电站直流系统中蓄电池容量难以预测的问题,本文引入神经网络对蓄电池容量进行预测研究。为实现对变电站直流系统蓄电池容量的准确预测,首先对当前国内外蓄电池容量预测研究现状进行分析,总结国内外研究成果进行下一步研究工作;然后,在吸取各研究经验的基础上,对蓄电池容量预测模型进行构建。在构建该预测模型时,本文主要采用模糊神经网络与改进学习算法构建起整个蓄电池容量预测模型;最后,通过具体的仿真实验对上述模型的可行性和科学性进行验证,进而验证本文构建的模型在预测变电站直流系统蓄电池容量上的正确性。结果表明,本文构建的神经网络模型在预测方面与实际的均方根误差最小,进而验证了本文构建算法的精度与实际最为接近。由此说明本文构建模型的科学性和正确性。通过以上的研究,为变电站直流系统蓄电池容量预测工作提供参考。
Aiming at the problem of unpredictable capacity of battery in DC system of substation at present,the battery capacity prediction based on neural network is introduced. In order to achieve accurate prediction of battery capacity of DC system in substation,firstly,the current research status of battery capacity prediction at home and abroad was analyzed,and the next research work was summarized. In the construction of the prediction model,on fuzzy neural network and improved learning algorithm are adopted; finally,the feasibility and scientificity of the above model are verified by specific simulation experiments,The validity of the model in predicting the storage capacity of DC system in substation is verified. The results show that the neural network model constructed in this paper has the smallest root mean square error in predicting and the actual,thus verifying the accuracy of the algorithm constructed in this paper is the closest to the actual. It shows that this model is scientific and correct. Through the above research,it provides reference for battery capacity prediction of DC system in substation.
Aiming at the problem of unpredictable capacity of battery in DC system of substation at present,the battery capacity prediction based on neural network is introduced. In order to achieve accurate prediction of battery capacity of DC system in substation,firstly,the current research status of battery capacity prediction at home and abroad was analyzed,and the next research work was summarized. In the construction of the prediction model,on fuzzy neural network and improved learning algorithm are adopted; finally,the feasibility and scientificity of the above model are verified by specific simulation experiments,The validity of the model in predicting the storage capacity of DC system in substation is verified. The results show that the neural network model constructed in this paper has the smallest root mean square error in predicting and the actual,thus verifying the accuracy of the algorithm constructed in this paper is the closest to the actual. It shows that this model is scientific and correct. Through the above research,it provides reference for battery capacity prediction of DC system in substation.
引文
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[13]张金国,王小君,朱洁,等.基于MIV的BP神经网络磷酸铁锂电池寿命预测[J].电源技术,2016,40(01):50-52.
[14]刘征宇,杨俊斌,张庆,等.基于QPSO-BP神经网络的锂电池SOC预测[J].电子测量与仪器学报,2013,27(03):224-228.
[15]陈年,王宏华,韩伟.基于GA-BP神经网络的光伏阵列MPPT研究[J].电测与仪表,2014,51(02):40-44.
[16]申彩英,胥帆,李刚.模糊C均值BP神经网络算法及其应用[J].中国农机化学报,2014,35(04):260-263+282.
[17]刘小强,李奇,程站立.基于BP神经网络的燃料电池电特性建模[J].应用能源技术,2009,(5):1-4.
[18]吴剑,张承慧,崔纳新.并联式混合动力汽车的BP网络实时能量管理[J].电机与控制学报,2008,(5):610-614.
[2]刘宁,张晶,赵圣芳.基于大变异GA-BP的MPPT的仿真与研究[J].制造业自动化,2017,39(07):111-113+132.
[3]余晓玲,王春玲,韩晓娟.基于数据挖掘技术的电池储能系统SOC状态评估[J].电器与能效管理技术,2017,(13):68-73.
[4]夏飞,马茜,张浩,等.改进D-S证据理论在电动汽车锂电池故障诊断中的应用[J].智能系统学报,2017,12(04):526-537.
[5]刘大同,周建宝,郭力萌,等.锂离子电池健康评估和寿命预测综述[J].仪器仪表学报,2015,36(01):1-16.
[6]林虹江,周步祥,冉伊,等.基于遗传优化BP神经网络算法的光伏系统最大功率点跟踪研究[J].电测与仪表,2015,52(05):35-40.
[7]朱正伟,周谢益,郭枫,等.基于遗传算法优化的RBF-BP神经网络在光伏阵列MPPT的研究[J].制造业自动化,2015,37(11):78-81.
[8]冯冬青,李卫帅.基于GA-BP神经网络的电池剩余电量的预测[J].计算机仿真,2011,28(12):323-326+334.
[9]徐艳民.基于BP-EKF算法的电池SOC估计[J].汽车技术,2018,(2):19-23.
[10]陈建新,候建明,王鑫,等.基于局部信息融合及支持向量回归集成的锂电池健康状态预测[J].南京理工大学学报,2018,42(01):48-55.
[11]周扬.基于优化Q学习算法的光储系统控制策略研究[J].电子世界,2018,(8):11-12+15.
[12]乔维德,凌兴宏.用于电动汽车电池SOC预测的BP神经网络模型[J].石家庄学院学报,2018,20(03):31-37.
[13]张金国,王小君,朱洁,等.基于MIV的BP神经网络磷酸铁锂电池寿命预测[J].电源技术,2016,40(01):50-52.
[14]刘征宇,杨俊斌,张庆,等.基于QPSO-BP神经网络的锂电池SOC预测[J].电子测量与仪器学报,2013,27(03):224-228.
[15]陈年,王宏华,韩伟.基于GA-BP神经网络的光伏阵列MPPT研究[J].电测与仪表,2014,51(02):40-44.
[16]申彩英,胥帆,李刚.模糊C均值BP神经网络算法及其应用[J].中国农机化学报,2014,35(04):260-263+282.
[17]刘小强,李奇,程站立.基于BP神经网络的燃料电池电特性建模[J].应用能源技术,2009,(5):1-4.
[18]吴剑,张承慧,崔纳新.并联式混合动力汽车的BP网络实时能量管理[J].电机与控制学报,2008,(5):610-614.