title: ÐÐ½Ð°Ð»Ð¸Ð· ÑÐ¾ÑÐ¾Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑÐµÐ¹ Ð¿Ð¾Ð²ÐµÑÑ Ð½Ð¾ÑÑÐ¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Analyze the photoreceiving surface using machine learning
Creators: name: ÐÐ°ÑÐ»Ð°Ð¼Ð¾Ð²Ð°, ÐÐ»ÐµÐºÑÐ°Ð½Ð´ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ ÑÐµÑÑ
Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÐµ ÑÐ»ÐµÐ¼ÐµÐ½ÑÑ
image classification
neural network
convolutional neural network
detection
solar cells

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¾ÑÐ¾Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑÐµÐ¹ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð´Ð»Ñ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ ÑÐ¾ÑÐ¾Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑÐµÐ¹ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸. ÐÐ½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð¾Ð±ÑÑÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑ YOLOv8 Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÑ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð². Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¾Ð»Ð½ÐµÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑ 96.6%. ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð° ÑÐ¾ÑÑÐ°Ð½ÑÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð² ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¾ÑÐ¼Ð°ÑÐ°Ñ, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð² Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÐ²Ð¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð¸Ñ Ð´Ð»Ñ ÑÐ°ÑÑÐµÑÐ° ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð²ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ ÑÐ¾ÑÐ¾ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»Ñ. Ð ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐµ Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°Ð¼Ð¸ Ð´Ð»Ñ ÑÐ°ÑÑÐµÑÐ¾Ð² Ð²ÑÑÐ¾Ð´Ð½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÑ Ð±Ð°ÑÐ°ÑÐµÐ¹ Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ÑÐ¾ÐºÐ¾Ð² ÐºÐ¾ÑÐ¾ÑÐºÐ¾Ð³Ð¾ Ð·Ð°Ð¼ÑÐºÐ°Ð½Ð¸Ñ Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸Ñ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÐ¼Ð¸ ÑÐ¾ÐºÐ°Ð¼Ð¸, Ð¸Ð·Ð¼ÐµÑÐµÐ½Ð½ÑÐ¼Ð¸ Ð½Ð° Ð¸Ð¼Ð¸ÑÐ°ÑÐ¾ÑÐµ ÑÐ¾Ð»Ð½ÐµÑÐ½Ð¾Ð³Ð¾ Ð¸Ð·Ð»ÑÑÐµÐ½Ð¸Ñ. ÐÐ¾Ð³ÑÐµÑÐ½Ð¾ÑÑÑ Ð¼ÐµÐ¶Ð´Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸ÑÐ¼Ð¸ ÑÐ¾ÐºÐ¾Ð² Ð¿ÑÐ¸ ÑÐ°Ð·Ð½Ð¾Ð¼ ÑÑÐ¾Ð²Ð½Ðµ Ð·Ð°ÑÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ¾Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑÐµÐ¹ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸ Ð½Ðµ Ð¿ÑÐµÐ²ÑÑÐ°ÐµÑ 7%. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð°Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ ÐºÐ¾Ð½ÑÐµÐ½ÑÑÐ°ÑÐ¾ÑÐ½ÑÑ ÑÐ¾ÑÐ¾ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÑÐ»ÐµÐ¹ Ð±Ð¾Ð»ÐµÐµ ÑÐµÐ¼ Ð² 2 ÑÐ°Ð·Ð°, Ð¿Ð¾ÑÑÐ¾Ð¼Ñ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð² Ð½Ð°ÑÑÐ½ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸ÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð² Ð¸Ð½Ð¶ÐµÐ½ÐµÑÐ½Ð¾Ð¹ Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÑ Ð±Ð°ÑÐ°ÑÐµÐ¹.
description: The purpose of this work is to analyze the photoreceiving surface using machine learning. A program for optical control of the photoreceiving surface has been developed. It uses a trained YOLOv8 neural network to detect solar cells. The accuracy of solar cell detection is 96.6%. The program can save results in various formats, which allows them to be used later to calculate the theoretical output data of the photovoltaic converter. In conjunction with programs for calculating the output parameters of solar cells, theoretical values of short-circuit currents were obtained and their comparison was made with experimental currents measured on a solar radiation simulator. The error between the current values at different levels of shading of the photoreceiving surface does not exceed 7%. The developed program allows to increase the accuracy of modeling the operation of concentrator photovoltaic modules by more than 2 times, so it can be used in scientific research, as well as in engineering practice for the development and optimization of solar cells.