深度学习算法在光伏电站无人机智能运维中应用

赖菲; 陈亚鹏; 单正涛; 吴涛; 徐创学; 何新; 王智微; 赵勇

doi:10.19666/j.rlfd.201904075

English

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深度学习算法在光伏电站无人机智能运维中应用

热力发电 2019年48卷第9期页码：139-144

智能电厂技术论坛 | 更新时间：2025-09-28

- 深度学习算法在光伏电站无人机智能运维中应用
- 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
  中图分类号： TM73
- 网络出版：2019-09-05，
  
  纸质出版：2019
- 稿件说明：
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赖菲, 陈亚鹏, 单正涛, 等. 深度学习算法在光伏电站无人机智能运维中应用[J]. 热力发电, 2019,48(9):139-144.

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.
赖菲, 陈亚鹏, 单正涛, 等. 深度学习算法在光伏电站无人机智能运维中应用[J]. 热力发电, 2019,48(9):139-144. DOI： 10.19666/j.rlfd.201904075.

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.

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