An orthogonal algorithm for abnormal data of photovoltaic production based on deep learning

Guangzhi Lai; Xiangying Xie; Haoran Li; Hongbin Xie; Shengxian Cao; Shulin Gong

doi:10.1117/12.2628539

7 March 2022 An orthogonal algorithm for abnormal data of photovoltaic production based on deep learning

Guangzhi Lai, Xiangying Xie, Haoran Li, Hongbin Xie, Shengxian Cao, Shulin Gong

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672J (2022) https://doi.org/10.1117/12.2628539
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

As an important part of new energy, the importance of solar energy is beyond doubt. However, due to the characteristics of photovoltaic power generation, most photovoltaic power stations are located in remote places, so it is difficult for personnel to check and clean them regularly. Aiming at the situation that the types of fallen leaves on photovoltaic panels are complex and difficult to clean up, an orthogonal algorithm for photovoltaic production abnormal data based on deep learning is proposed, and the model network method for quickly detecting the shelter of fallen leaves on photovoltaic panels and determining the shelter position is discussed. In this paper, an improved PP-YOLO is proposed to identify and classify the occlusion of photovoltaic modules. The original YOLO and the improved PP-YOLO are used to carry out the target detection experiment on the collected field data set of photovoltaic power station. The results show that the improved algorithm is effective, and the detection accuracy and recall rate are up to 96.3% and 94.2%. This method can provide technical support for real-time and accurate detection of photovoltaic panel intelligent cleaning.

Citation Download Citation

Guangzhi Lai, Xiangying Xie, Haoran Li, Hongbin Xie, Shengxian Cao, and Shulin Gong "An orthogonal algorithm for abnormal data of photovoltaic production based on deep learning", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672J (7 March 2022); https://doi.org/10.1117/12.2628539

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Target detection

Detection and tracking algorithms

Solar cells

Solar energy

Photovoltaics

Convolution

Performance modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years