Application of deep learning algorithm in intelligent operation and maintenance of UAVs in photovoltaic power plants

LAI Fei; CHEN Yapeng; SHAN Zhengtao; WU Tao; XU Chuangxue; HE Xin; WANG Zhiwei; ZHAO Yong

doi:10.19666/j.rlfd.201904075

English

您当前的位置：

首页 >

文章列表页 >

Application of deep learning algorithm in intelligent operation and maintenance of UAVs in photovoltaic power plants

Thermal Power Generation Vol. 48, Issue 9, Pages: 139-144(2019)

更新时间：2025-09-28

- Application of deep learning algorithm in intelligent operation and maintenance of UAVs in photovoltaic power plants
- 作者机构：
  
  1. 西安热工研究院有限公司
  2. 国家电投珠海横琴热电有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201904075
  CLC： TM73
- Published Online：05 September 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
LAI Fei, CHEN Yapeng, SHAN Zhengtao, et al. Application of deep learning algorithm in intelligent operation and maintenance of UAVs in photovoltaic power plants[J]. Thermal Power Generation, 2019, 48(9): 139-144.
DOI：

LAI Fei, CHEN Yapeng, SHAN Zhengtao, et al. Application of deep learning algorithm in intelligent operation and maintenance of UAVs in photovoltaic power plants[J]. Thermal Power Generation, 2019, 48(9): 139-144. DOI： 10.19666/j.rlfd.201904075.

摘要

针对当前光伏电站运维存在的效率低、成本高等问题

提出一套结合无人机与人工智能对光伏电站进行智能维护的方法。首先通过实时监控光伏阵列的运行状态和实时数据的智能分析

及时发现故障所在阵列

然后通过无人机巡航对故障所在光伏阵列进行图像采集

并利用深度学习算法对图像进行故障识别

判断发生故障的具体光伏单元及相应的故障类型

以便尽早发现隐形故障

保障电站高效安全运行

降低运维成本

提高巡检效率。该方法对于未来光伏电站运维工作的方向把握具有重要的指导意义。

Abstract

In view of the problems of low efficiency and high cost occurred in operation and maintenance of the current photovoltaic power stations

a set of methods for smart maintenance of the photovoltaic power station in combination with unmanned aerial vehicle and artificial intelligence is proposed. In this method

real-time monitoring of the operation status of photovoltaic arrays and intelligent analysis of real-time data are carried out to find the faulty arrays timely. Then

by UAV cruise

the photovoltaic arrays where the faults are located are collected and the specific photovoltaic units and the corresponding types of faults are determined by deep learning algorithm

so as to find the hidden faults as early as possible

ensure the high-efficiency and safe operation of the power station

reduce the operation and maintenance cost and improve the inspection efficiency. This method has guiding significance for the future operation and maintenance of photovoltaic power stations.

关键词

Keywords

references

Views

412

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

Research on development and engineering application of gas turbine performance monitoring and fault early warning system

Condition based maintenance for power station equipments based on equipment characteristic value

Construction scheme of power online technical supervision platform based on big data and artificial intelligence technology

Application of machine learning in state analysis of power plant equipment

Design and simulation analysis of self-adaptive photovoltaic support for photovoltaic power station

Related Author

YANG Zhipeng

FAN Xuefei

HAO Ning

DING Shenghao

LIU Ping

TIAN Shuyun

XIE Yuesheng

WAN Zhentian

Related Institution

Shanghai Caojing Thermal Power Co., Ltd

Shanghai Power Generation Equipment Complete Design and Research Institute Co., Ltd

China Huaneng Group Co., Ltd

Inner Mongolia Electric Power Research Institute

China Power Investment Corporation Zhuhai Hengqin Thermal Power Co., Ltd

AI问答

⁰