摘要
针对变电站风机远程控制的问题,提出一种基于卷积神经网络的调度优化方法。首先收集风机的运行功率、温度、湿度以及电压等数据;然后利用卷积神经网络理论计算已收集数据,并得到风机的运行状态;最后依据风机运行状态,对智能用电开关、智能空开进行远程控制。研究结果显示,基于卷积神经网络的远程调度方法能全面了解风机运行状态,数据拟合度和压缩率较高,并对风机进行精准的远程调度,而且响应时间较短。
Aiming at the problem of remote control of substation fan,a scheduling optimization method based on convolution neural network is proposed.Firstly,data on the operating power,temperature,humidity and voltage of the fan were collected.Then,the convolutional neural network theory was used to calculate the collected data and obtain the operating state of the fan.Finally,according to the operating status of the fan,the intelligent power switch and intelligent air opening were remotely controlled.The results show that the remote scheduling method based on convolutional neural network can comprehensively understand the operating status of the wind turbine,the data fitting degree and compression ratio are high,and the remote scheduling of the wind turbine is accurate,and the response time is short.
作者
郝越
马义松
李健
HAO Yue;MA Yisong;LI Jian(Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen 518000,China)
出处
《电工技术》
2023年第10期14-16,共3页
Electric Engineering
关键词
卷积神经网络
变电站
风机
远程控制
convolution neural network
substation
fan
remote control
