智能巡检机器人巡检电力线路时可能受到电磁干扰而影响工作甚至发生故障,为有效地完成智能巡检机器人电磁兼容故障的诊断,提出一种基于改进灰狼算法(improved grey wolf optimizer,IGWO)优化BP神经网络(IGWO-BP)的故障诊断模型。由于智...智能巡检机器人巡检电力线路时可能受到电磁干扰而影响工作甚至发生故障,为有效地完成智能巡检机器人电磁兼容故障的诊断,提出一种基于改进灰狼算法(improved grey wolf optimizer,IGWO)优化BP神经网络(IGWO-BP)的故障诊断模型。由于智能巡检机器人电磁兼容故障征兆与故障原因之间具有复杂的非线性关系,采用一般BP神经网络诊断模型存在着收敛速度较慢,易陷入局部最优,诊断准确率偏低的缺陷。针对以上问题,利用IGWO-BP的权值与阈值,将优化后的BP神经网络应用于智能巡检机器人电磁兼容故障诊断。仿真结果表明,相比于GWO-BP神经网络和一般BP神经网络,IGWO-BP神经网络诊断模型收敛速度加快,网络泛化能力增强,故障诊断准确率提高。展开更多
In the present study,the performance of the GRAPES model in wind simulation over south China was assessed.The simulations were evaluated by using surface observations and two sounding stations in south China.The resul...In the present study,the performance of the GRAPES model in wind simulation over south China was assessed.The simulations were evaluated by using surface observations and two sounding stations in south China.The results show that the GRAPES model could provide a reliable simulation of the distribution and diurnal variation of the wind.It showed a generally overestimated southerly wind speed especially over the Pearl River Delta region and the south of Jiangxi Province as well as the coastal region over south China.GRAPES also exhibited a large number of stations with the opposite surface wind directions over the east of Guangxi and the south of Jiangxi during the nocturnalto-morning period,as well as an overall overestimation of surface wind over the coastal regions during the afternoon.Although GRAPES could simulate the general evolutional characteristics of vertical wind profile,it underestimated wind speed above 900 hPa and overestimated wind speed below 900 hPa.Though the parameterization scheme of gravity wave drag proved to be an effective method to alleviate the systematic deviation of wind simulation,GRAPES still exhibited large errors in wind simulation,especially in the lower and upper troposphere.展开更多
文摘智能巡检机器人巡检电力线路时可能受到电磁干扰而影响工作甚至发生故障,为有效地完成智能巡检机器人电磁兼容故障的诊断,提出一种基于改进灰狼算法(improved grey wolf optimizer,IGWO)优化BP神经网络(IGWO-BP)的故障诊断模型。由于智能巡检机器人电磁兼容故障征兆与故障原因之间具有复杂的非线性关系,采用一般BP神经网络诊断模型存在着收敛速度较慢,易陷入局部最优,诊断准确率偏低的缺陷。针对以上问题,利用IGWO-BP的权值与阈值,将优化后的BP神经网络应用于智能巡检机器人电磁兼容故障诊断。仿真结果表明,相比于GWO-BP神经网络和一般BP神经网络,IGWO-BP神经网络诊断模型收敛速度加快,网络泛化能力增强,故障诊断准确率提高。
基金National Key R&D Program of China(2018YFC1507602)National Natural Science Foundation of China(41505084,41875079)+1 种基金Guangzhou Science and Technology Project(201804020038)Guangdong Province Public Welfare Research and Capacity Construction Project(2017B020218003)
文摘In the present study,the performance of the GRAPES model in wind simulation over south China was assessed.The simulations were evaluated by using surface observations and two sounding stations in south China.The results show that the GRAPES model could provide a reliable simulation of the distribution and diurnal variation of the wind.It showed a generally overestimated southerly wind speed especially over the Pearl River Delta region and the south of Jiangxi Province as well as the coastal region over south China.GRAPES also exhibited a large number of stations with the opposite surface wind directions over the east of Guangxi and the south of Jiangxi during the nocturnalto-morning period,as well as an overall overestimation of surface wind over the coastal regions during the afternoon.Although GRAPES could simulate the general evolutional characteristics of vertical wind profile,it underestimated wind speed above 900 hPa and overestimated wind speed below 900 hPa.Though the parameterization scheme of gravity wave drag proved to be an effective method to alleviate the systematic deviation of wind simulation,GRAPES still exhibited large errors in wind simulation,especially in the lower and upper troposphere.
