Photovoltaic panel dust accumulation detection

How to detect surface dust on solar photovoltaic panels?

At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image segmentation and instance segmentation, super-resolution image generation, multispectral and thermal infrared imaging, and deep learning methods.

Can a convolutional neural network detect solar panel dust accumulation?

Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%.

What is dust accumulated PV panels?

Dust accumulated PV panels — An integrated survey of factors, mathematical model, and proposed cleaning mechanisms. Handy information to readers, engineers, and practitioners. A possible sustainable solution to challenges of water availability and PV systems cleaning mechanisms.

Can a CNN detect dust accumulation on PV panels?

Onim et al. proposed a CNN to detect dust accumulation on PV panels using a dataset of images of dusty and clean panels. The results demonstrated high accuracy levels . Selvaraj et al. proposed a method for accurate diagnosis of environmental faults using CNN and thermal images for classification of these faults . ... ...

Do dust accumulated PV panels affect performance?

Accumulation and aggregation of dust particles on PV panels — A significant influence on the performance. Dust accumulated PV panels — An integrated survey of factors, mathematical model, and proposed cleaning mechanisms. Handy information to readers, engineers, and practitioners.

How is solar photovoltaic panel dust detection data processed?

In terms of data processing, we adopted the solar photovoltaic panel dust detection dataset and divided the data into training, validation, and testing sets in a strict 7:2:1 ratio to ensure that the quality and quantity of training, validation, and testing data are fully guaranteed.