摘要
针对沙漠光伏电站规模大、工作环境恶劣,不利于工作人员掌握电站实时运行状态,存在故障处理不及时、易发生安全事故等问题,基于物联网技术和神经网络算法,设计了光伏电站远程监测及功率预测系统,实现电站运行状态数据的实时采集,通过无线通信技术进行远程传输,最终实现光伏电站的远程监测和智能管理。提出了SCSSA-CNN-BiLSTM预测模型,实现对光伏输出功率的精准预测。系统运行稳定可靠,数据采集误差小于1%,光伏输出功率预测平均绝对误差小于0.3,对于指导工作人员优化电网调配、提高电站运营效率具有实际应用价值。
In response to the large scale and harsh working environment of desert photovoltaic power stations,which are not conducive to the real-time operation status of the power station for staff,and have problems such as untimely fault handling and easy occurrence of safety accidents,a remote monitoring and power prediction system for photovoltaic power stations is designed based on Internet of Things technology and neural network algorithms to achieve real-time collection of power station operation status data,remote transmission through wireless communication technology,and ultimately achieve remote monitoring and intelligent management of photovoltaic power stations.Propose the SCSA-CNN BiLSTM prediction model to achieve accurate prediction of photovoltaic output power.The system runs stably and reliably,with a data collection error of less than 1%and an average absolute error of less than 0.3 for predicting photovoltaic output power.It guides staff to optimize power grid allocation,improve power station operation efficiency,and has practical application value.
作者
梁伟
马兴海
陈海宏
马映江
王志强
LIANG Wei;MA Xinghai;CHEN Haihong;MA Yingjiang;WANG Zhiqiang(Gansu Longyuan New Energy Co.,Ltd.,Gansu Lanzhou 730050,China)
出处
《工业仪表与自动化装置》
2025年第5期117-123,共7页
Industrial Instrumentation & Automation
