Open Access

ARTICLE

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

by Yongcan Zhu^1,2, Jiawen Wang¹, Ye Zhang^1,2, Long Zhao¹, Botao Jiang¹, Xinbo Huang^1,*

1 College of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, China
2 Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment, Xi’an, 710048, China

* Corresponding Author: Xinbo Huang. Email:

(This article belongs to the Special Issue: Fault Diagnosis and State Evaluation of New Power Grid)

Energy Engineering 2024, 121(4), 895-911. https://doi.org/10.32604/ee.2023.041002

Received 07 April 2023; Accepted 17 July 2023; Issue published 26 March 2024

Abstract

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment of a residual ice and snow recognition process. This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process. The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures. The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules. In conclusion, this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules. This breakthrough is of utmost significance for PV power plants, as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice. By proactively managing snow and ice coverage, PV power plants can optimize energy production and minimize downtime, ensuring a sustainable and reliable renewable energy supply.

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Keywords

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