Abstract
The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel’s surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in 12% of effectiveness, with the corresponding financial impact.
Highlights
Accelerated demographic and economic growth in several countries has led to an increase in the electrical energy demand
To compute the algorithm robustness regarding variations of camera position and lightning conditions, the images were acquired at two distances from the PV-structure: at 3 m and 4 m
In contrast with Tsanakas’ algorithm, in our work, we automatically find the regions of interest (ROI) by applying the proposed PV-module detection algorithm
Summary
Accelerated demographic and economic growth in several countries has led to an increase in the electrical energy demand. The future of the photovoltaic plant inspection is focused on the maintenance robotics with emphasis on robots able to detect and correct damaged electric equipment [2]. Nowadays, both autonomous robotics and tele-operated machines, despite being useful, have found only limited application because of payload capacity of platforms (mainly aerial platforms), limited access and rugged environments [3]. The system refines the PV-structure estimation using a filter, which aim is to remove all connected components that have fewer than P pixels Such parameter, P, is determined off-line and it is related to the module size. This step returns a binary mask associated with the surface of PV-modules
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