Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model

TL;DR

This work introduces a Vision Transformer (ViT)–based framework for automatic surface defect detection on solar PV panels and wind turbine blades using drone imagery. By comparing ViT to multiple pre-trained CNNs, the study demonstrates superior accuracy (above 97%) and efficiency across two real-world datasets, with solar and wind assets achieving 98.66% and 97.33% accuracy respectively. The approach includes detailed dataset descriptions, image augmentation, and hyperparameter tuning, highlighting ViT’s potential for scalable, low-cost monitoring in large renewable-energy plants. The findings support practical deployment for proactive asset maintenance, reduced downtime, and extended asset lifespans in renewable energy deployments.

Abstract

The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. {Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects.} The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97\% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

Figures (11)

• Figure 1: Proposed framework for monitoring and detection of damages in the solar panels and wind turbines.
• Figure 2: ViT framework with wind turbine blade image as input.
• Figure 3: Architectural diagram of transformer encoder with multi-head attention consists of several attention layers running in parallel and scaled dot-product attention module.
• Figure 4: Process flow of considered DL models.
• Figure 5: Surface damages on wind turbine blades.