UAV data transfer at the station allows to reduce UAV energy consumption in flight. The base station has two levels: the top one and the bottom one. The construction of base station allows
The observatory station is based on the container with a photovoltaic installation mounted on three walls to fulfill the electricity demand for the station and integrated systems. A container
Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple
Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in
Unmanned Aerial Vehicle (UAV)-assisted data collection will be a prospective solution for photovoltaic systems. In this paper, based on Deep Reinforcement Learning (DRL), we propose a UAV-assisted scheme, which
In cellular networks, a set of Base Stations (BSs) might be out of service and failed in the aftermath of natural disasters. One of the promising solutions to fix this situation is
Unmanned aerial vehicles are widely implanted to reduce maintenance costs in photovoltaic plants, leading suitable information for fault detection and diagnosis. This paper
The implemented UAV-based system for inspection of large-scale PV systems consists of an UAV with a set of sensors in different forms and on-board processors, a digital light visible single
2.2. Hot-Spot Fault Detection Based on the Infrared Image Features of Photovoltaic Panels In a small number of photovoltaic panel detection tasks, many scholars are still using infrared
Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput.
Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation efficiency and even cause
Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation
Partial infrared photovoltaic image dataset. (a) The UAV took photos along the horizontal direction of the photovoltaic panel. (b) The UAV took photos along the tilt angle of
This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics.
Focus was shed on UAV-based approaches, that can support PV plant diagnostics using imaging techniques and data analytics. In this context, the essential equipment needed and the sensor requirements (parameters and resolution) for the diagnosis of failures in monitored PV systems using UAV-based approaches were outlined.
Unmanned Aerial Vehicles (UAVs) have been recently proposed for PV inspections. In the past decades, research made significant steps forward concerning the development of UAVs for monitoring applications, including the inspection of power transmission lines , gas and oil pipelines , precision agriculture , and bridges .
Therefore, early fault diagnosis (detection and classification) using a UAV inspection system is crucial for PV plant's O&M to ensure adequate performance, prevent extension of defects to healthy areas and reduce the monitoring cost.
Another important criterion for the design of a UAV platform is the interoperability and communication protocols among different modules: flight controller, platform stabilization, heading accuracy, autonomous operations, etc. Table 5. Main sensor that can be integrated to the UAV for fault diagnosis in PV systems.
The UAV selected for this simulation is the DJI S900. The route is designed to analyse each PV panel in the same FOV conditions, adapting its hight depending on the PV positioning and FOV conditions. Therefore, the route is based on 432 points with different height and coordinates defined by the GPS and RTK systems to compare both results.
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