A Novel Methodology for Determination of Soiling on PV Panels by Means of Grey Box Modelling

Rodil, Marc Castella; Montenegro, Juan Patarroyo; Kampouropoulos, Konstantinos; Andrade, Fabio; Romeral, L.

doi:10.1109/iecon.2019.8927584

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Gray box modeling is a method that combines theoretical models and empirical data, and can be used to describe the performance of PV panels under various environmental and voltage conditions. Castellà Rodil et al [88] developed a new method based on data analysis, which used the information collected by the existing sensor equipment in the PV power station to generate a gray box model for each PV panel, and to evaluate the occurrence and impact of dust with fitting coefficients. This method leveraged the data provided by the existing supervisory control and data acquisition (SCADA) system, and followed the steps of parameter selection, state matrix construction and update, similarity operator design, etc., to achieve high-accuracy and fast-response dust monitoring.…”

Section: Gray Box Model Methodsmentioning

confidence: 99%

“…The advantage of the method in [88] is that it does not require any additional equipment or human intervention, and it can utilize the existing data acquisition system from the PV power plant. In addition, it can analyze each PV panel individually to determine dust distribution and trends.…”

Section: Arduino-based Dust Removal Systemmentioning

confidence: 99%

“…The limitation of the approach in [88] is the need to adapt the theoretical model of PV panels to different types and sizes of panels. It is also affected by the quality and completeness of the PV plant data.…”

Section: Arduino-based Dust Removal Systemmentioning

confidence: 99%

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A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

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Section: Gray Box Model Methodsmentioning

confidence: 99%

Section: Arduino-based Dust Removal Systemmentioning

confidence: 99%