针对输电线路光伏监控装置的智能能量预测调控技术研究

王传宇; 刘子岩; 林泽权; 郝强岩; 汪云云; 裴刚

doi:doi:10.19912/j.0254-0096.tynxb.2024-2130

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针对输电线路光伏监控装置的智能能量预测调控技术研究

更新时间：2026-07-22

- 针对输电线路光伏监控装置的智能能量预测调控技术研究
- Vol. 47, Issue 4, Pages: 644-652(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2130
  CLC： TK519
- Online First：08 May 2026，
  
  Published：2026
- 稿件说明：
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王传宇, 刘子岩, 林泽权, et al. 针对输电线路光伏监控装置的智能能量预测调控技术研究[J]. 2026, 47(4): 644-652.
DOI：

王传宇, 刘子岩, 林泽权, et al. 针对输电线路光伏监控装置的智能能量预测调控技术研究[J]. 2026, 47(4): 644-652. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2130.

摘要

目前光伏供能输电线路监测装置存在受天气影响显著、离线率过高的问题

针对这种孤立光伏能源系统

提出一套智能能量预测及调控技术

利用历史天气数据及未来天气预报

采用新型组合神经网络模型VMD-RIME-LSTM对未来光伏发电量进行预测;结合设备能量信息以及线路监控需求智能调节设备的工作模式

在保障监控质量需求前提下对可用能量进行合理规划

实现对太阳能资源的更有效利用

大幅提高设备工作的稳定性

降低离线率。同时根据该技术搭建实验样机平台及全年模拟模型

验证所提技术能有效降低在线监控装置离线率

提高其工作质量。

Abstract

Photovoltaic monitoring devices for power supply and transmission lines often experience significant weather-related issues and high offline rates. To mitigate these problems in isolated PV systems

an intelligent energy prediction and control technology is introduced. This technology uses historical and forecasted weather data

combined with a novel VMD-RIME-LSTM neural network model

to predict future PV output. It intelligently adjusts device operation modes based on energy information and monitoring needs

ensuring high-quality monitoring and efficient solar resource utilization. This enhances device stability and reduces offline rates. An experimental platform and a full-year simulation model were developed to verify the technology’s effectiveness in lowering offline rates and improving operational quality.

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references

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