Work

A Distributed Photovoltaic Operation and Maintenance Cloud Platform for PV Aerial Inspections With Sparse Industrial Data

Year: 2025

Type: article

Abstract: Distributed photovoltaic (DPV) power sites in industrial parks are characterized by dispersed layouts, practical fault detection environments, and high safety requirements. Conventional manual DPV O&M systems using handheld sensors are inefficient, expensive, and struggle with fault detection due to sparse industrial data and uni-modal information limitations. To this, this paper proposes an innovative advanced algorithm for DPV fault detection in industrial parks, utilizing a new sparse industrial dataset, "SolarPark," collected via multi-modal UAVs and annotated through a multi-expert process with uncertainty scoring. By fusing the Convolutional Block Attention Module (CBAM), Bidirectional Feature Pyramid Network (BiFPN), Ghost modules, the algorithm enhances attention to critical photovoltaic fault-related channel information, strengthens multi-scale photovoltaic fault feature fusion, and achieves lightweight efficiency. Combined with multi-modal UAV videos, the proposed industrial DPV fault detection algorithm achieves a precision of 95.4%, effectively ensuring the efficiency of DPV power sites in data-scarce industrial scenarios. Extensive experiments on the developed cloud platform confirm the proposed algorithm’s efficient, cost-effective, and easy to deploy for aerial inspections of DPV O&M systems.

Source: IEEE Access

DOI: 10.1109/access.2025.3561234