基于VMD和Bat-KELM的仿真变电站蓄电池剩余寿命预测OA北大核心
Remaining Useful Life Prediction of Simulation Substation Batteries Based on VMD and Bat-KELM
仿真变电站蓄电池的工作模式呈现间歇非连续性,导致电池性能在退化过程中存在容量再生现象,退化规律具有非平稳性和随机性,增大了蓄电池精确剩余寿命RUL(remaining useful life)的难度.针对存在容量再生现象的蓄电池剩余寿命预测问题,提出了变分模态分解VMD(variational mode decomposition)和蝙蝠(Bat)优化核极限学习机 KELM(kernel extreme learning machine)组合的预…查看全部>>
Simulation substation batteries often work under discontinuous operation conditions,which will result in capacity regeneration of batteries during their performance degradation.The degradation of batteries shows nonstationary and random characteristics,leading to a low prediction accuracy for the remaining useful life(RUL).Aimed at the problem of RUL prediction of batteries with capacity regeneration,a prediction method is proposed based on variational mode …查看全部>>
任罡;季宁;胡晓丽;李世倩;张洁华;吴祎
国网江苏省电力有限公司技能培训中心,苏州 215004国网江苏省电力有限公司技能培训中心,苏州 215004国网江苏省电力有限公司技能培训中心,苏州 215004国网江苏省电力有限公司技能培训中心,苏州 215004国网江苏省电力有限公司技能培训中心,苏州 215004南京邮电大学自动化学院,南京 210023||南京邮电大学人工智能学院,南京 210023
动力与电气工程
仿真变电站蓄电池剩余寿命预测变分模态分解核极限学习机
Simulation substationbatteryremaining useful life(RUL)predictionvariational mode decomposition(VMD)kernel extreme learning machine(KELM)
《电源学报》 2024 (4)
251-259,9
国网江苏省电力有限公司科技项目(J2021020)This work is supported by Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd under the grant J2021020
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