摘要
为了准确对巡检机器人穿越预定杆塔所需要的能量进行SOC估计,通过分析线路工况和实验数据,建立巡检机器人能耗模型。考虑到巡检机器人从起始杆塔到预定杆塔之间的累计误差会逐渐增大,导致抵达预定杆塔时无法对巡检机器人锂电池SOC准确估计。因此,结合巡检机器人能耗状态方程和锂电池量测模型,并采用扩展卡尔曼滤波的方法对巡检机器人锂电池SOC能耗的理论值进行迭代,同时,也引入次优渐消因子降低不确定参数的误差,提高SOC估计精度,且SOC估计误差均在1.7%以下。
In order to accurately estimate the SOC of the energy required for the inspection robot to cross the predetermined tower,the energy consumption model of the inspection robot is established by analyzing the line conditions and experimental data.Considering that the cumulative error of the inspection robot from the starting tower to the scheduled tower will gradually increase,it is impossible to accurately estimate the SOC of the lithium battery of the inspection robot when it arrives at the scheduled tower.Therefore,combined with the energy consumption state equation of inspection robot and lithium battery measurement model,and using the extended Kalman filter method to iterate the theoretical value of lithium battery SOC energy consumption of inspection robot,at the same time,the suboptimal fading factor is introduced to reduce the error of uncertain parameters and improve the SOC estimation accuracy,and the SOC estimation error is less than 1.7%.
作者
李雨鑫
吴功平
刘家阳
林科茂
LI Yu-xin;WU Gong-ping;LIU Jia-yang;LIN Ke-mao(School of Power and Mechanical Engineering Wuhan University,Hubei Wuhan 430072,China)
出处
《机械设计与制造》
北大核心
2023年第11期253-257,262,共6页
Machinery Design & Manufacture
基金
南方电网公司重点科技项目(JWKJHT-1801003)。
关键词
巡检机器人
能耗模型
扩展卡尔曼滤波
次优渐消因子
Inspection Robot
Energy Consumption Model
Extended Kalman Filter
Variable Forgetting Factor
