PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production

• URL: http://arxiv.org/abs/2411.02997v1
• Date: Tue, 05 Nov 2024 10:58:37 GMT
• Title: PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production
• Authors: Eiffat E Zaman, Rahima Khanam,
• Abstract summary: This study presents PV-faultNet, a lightweight Convolutional Neural Network (CNN) architecture optimized for efficient and real-time defect detection in photovoltaic (PV) cells. The model includes only 2.92 million parameters, significantly reducing processing demands without sacrificing accuracy. It achieved high performance with 91% precision, 89% recall, and a 90% F1 score, demonstrating its effectiveness for scalable quality control in PV production.
• Score: 0.0
• License:
• Abstract: The global shift towards renewable energy has pushed PV cell manufacturing as a pivotal point as they are the fundamental building block of green energy. However, the manufacturing process is complex enough to lose its purpose due to probable defects experienced during the time impacting the overall efficiency. However, at the moment, manual inspection is being conducted to detect the defects that can cause bias, leading to time and cost inefficiency. Even if automated solutions have also been proposed, most of them are resource-intensive, proving ineffective in production environments. In that context, this study presents PV-faultNet, a lightweight Convolutional Neural Network (CNN) architecture optimized for efficient and real-time defect detection in photovoltaic (PV) cells, designed to be deployable on resource-limited production devices. Addressing computational challenges in industrial PV manufacturing environments, the model includes only 2.92 million parameters, significantly reducing processing demands without sacrificing accuracy. Comprehensive data augmentation techniques were implemented to tackle data scarcity, thus enhancing model generalization and maintaining a balance between precision and recall. The proposed model achieved high performance with 91\% precision, 89\% recall, and a 90\% F1 score, demonstrating its effectiveness for scalable quality control in PV production.

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