基于数字孪生技术的分布式光伏智慧运维一体化平台

Integrated platform for intelligent operation and maintenance of distributed photovoltaic based on digital twin technology

摘要

在“双碳”目标推动下，分布式光伏产业快速发展，但分散化布局与运维效率瓶颈制约其规模化应用。提出一种融合数字孪生、无人机巡检、AI诊断与组件级监控的智慧运维一体化平台，通过“三层四维”架构实现全流程智能化管理。感知层采用高精度传感器与无人机群协同采集数据，网络层依托5 G+APN专网保障数据毫秒级传输，应用层通过数字孪生三维建模与YOLOv5算法实现缺陷智能识别。关键技术包括5 cm精度倾斜摄影建模、ORB-SLAM3空地协同巡检算法、温差超过3℃热斑检测，以及TDengine时序数据库支撑的百亿级数据秒级查询。平台通过“无人机初筛-单兵复核”模式将单电站巡检耗时从2 d缩短至4 h,组串级故障漏检率从17%降至3%以下，AI诊断准确率达92%,有效解决串联组串“短板效应”,使电站运维成本降低35%以上。该平台为分布式光伏提供了从数据采集、智能诊断到决策支持的系统性解决方案，推动运维模式从“事后维修”向“预测性维护”转型，对提升新能源产业智能化管理水平具有重要实践价值。

Abstract

Driven by the “dual carbon” goals, the distributed photovoltaic(PV) industry has developed rapidly. However, its large-scale application is hindered by the challenges of decentralized layouts and the limitations of operational and maintenance(O&M) efficiency. To address these issues, an intelligent integrated O&M platform that integrates digital twin technology, UAV inspection, AI-based diagnosis, and component-level monitoring was proposed. A “three-layer, four-dimensional” architecture is established to enable full-process intelligent management. Specifically, the perception layer employs high-precision sensors and UAV fleets for collaborative data acquisition; the network layer leverages 5G combined with APN private networks to ensure millisecond-level data transmission; and the application layer implements intelligent defect detection through digital twin-based 3D modeling and the YOLOv5 algorithm.Key technologies underpinning this platform include 5 cm-precision oblique photography modeling, the ORB-SLAM3 air-ground collaborative inspection algorithm, hot spot detection with temperature differences exceeding 3℃, and sub-second queries of billion-level time-series data supported by the TDengine database. Through a “UAV preliminary screening-manual verification” workflow, the platform reduces the inspection time per power station from 2 days to 4 hours, lowers the string-level fault omission rate from 17% to below 3%, and achieves 92% diagnostic accuracy with AI. This approach effectively mitigates the “weakest-link effect” inherent in series-connected PV strings, resulting in a reduction of O&M costs by more than 35%. The proposed platform provides a comprehensive solution covering data acquisition, intelligent diagnosis, and decision-making support for distributed PV systems. It facilitates the transition of O&M practices from reactive maintenance to predictive maintenance and offers significant practical value for advancing intelligent management within the new energy sector.

关键词

分布式光伏 / 智慧运维 / 数字孪生 / AI诊断 / 无人机巡检

Key words

distributed photovoltaic / intelligent operation and maintenance / digital twin / AI diagnosis / UAV inspection

Author summay

何剑峰(1978—), 男, 高级工程师, 学士, 主要从事新能源发电站运维管理工作。E-mail: [e-mail]