浙江大学学报(工学版)
浙江大學學報(工學版)
절강대학학보(공학판)
JOURNAL OF ZHEJIANG UNIVERSITY(ENGINEERING SCIENCE)
2015年
4期
670-675,739
,共7页
付兴伟%吴功平%周鹏%于娜
付興偉%吳功平%週鵬%于娜
부흥위%오공평%주붕%우나
巡视机器人%线路环境参数%能耗估计%自适应卡尔曼滤波
巡視機器人%線路環境參數%能耗估計%自適應卡爾曼濾波
순시궤기인%선로배경삼수%능모고계%자괄응잡이만려파
inspection robot%working parameters of transmission line%energy-consumption estimation%adaptive Kalman filter
为了对机器人巡检完预定杆塔所需的能耗进行准确的估计,对巡视机器人进行受力分析,利用积分法得到基于线路的环境参数(档段的水平档距、档段高差等)的单档段内的能耗模型;通过实验验证了模型的可行性.通过分析可知,需要巡检的杆塔越多,经过每个档段误差的积累,从起始杆塔截止到终止杆塔的能耗理论值与测量值之间的相对误差越来越大,导致无法准确地估计巡检完预定杆塔所需的能耗.基于自适应卡尔曼滤波器对由能耗模型得到的能耗理论值进行迭代修正,将相对误差控制在1.05%左右,提高了估计精度.
為瞭對機器人巡檢完預定桿塔所需的能耗進行準確的估計,對巡視機器人進行受力分析,利用積分法得到基于線路的環境參數(檔段的水平檔距、檔段高差等)的單檔段內的能耗模型;通過實驗驗證瞭模型的可行性.通過分析可知,需要巡檢的桿塔越多,經過每箇檔段誤差的積纍,從起始桿塔截止到終止桿塔的能耗理論值與測量值之間的相對誤差越來越大,導緻無法準確地估計巡檢完預定桿塔所需的能耗.基于自適應卡爾曼濾波器對由能耗模型得到的能耗理論值進行迭代脩正,將相對誤差控製在1.05%左右,提高瞭估計精度.
위료대궤기인순검완예정간탑소수적능모진행준학적고계,대순시궤기인진행수력분석,이용적분법득도기우선로적배경삼수(당단적수평당거、당단고차등)적단당단내적능모모형;통과실험험증료모형적가행성.통과분석가지,수요순검적간탑월다,경과매개당단오차적적루,종기시간탑절지도종지간탑적능모이론치여측량치지간적상대오차월래월대,도치무법준학지고계순검완예정간탑소수적능모.기우자괄응잡이만려파기대유능모모형득도적능모이론치진행질대수정,장상대오차공제재1.05%좌우,제고료고계정도.
A mechanical analysis for the inspection robot on the line was conducted in order to accurately estimate the energy consumption w hich is required to ensure the w hole inspection process . T hen the mathematical model of energy consumption was deduced based on the line parameters of working conditions (span between the adjacent towers ,height difference of the adjacent towers ,and so on) by the integral method .The feasibility of the model was verified by experiment .The analysis results suggest that the more towers need to inspect the bigger relative error between the theory value and the measured value of the energy consumption from the start tower until terminate tower is ,which is caused by the cumulated error of the every line section between the adjacent towers .The energy consumption required for the whole inspection process cannot be accurately estimated .The theory value of the energy consumption from the start tower until terminate tower was iteratively corrected by the adaptive Kalman filter .The relative error was controlled around 1 .05% ,and the estimation accuracy was improved .