Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework

Abstract

The growing prevalence of the photovoltaic (PV) systems has intensified the focus on fault prediction and health management within both the academic and industrial realms. Electroluminescence (EL) imaging technology, recognized as an advanced detection method, has substantiated its efficiency and practicality in identifying diverse defects. In this study, we introduce a novel framework for anomaly detection in the PV panel systems, leveraging multiscale linear attention and scale distribution alignment learning (MLA-SDAL). Initially, we employ a feature extraction framework based on the multihead linear attention to facilitate the deep-level feature modeling. This network excels in the high-dimensional feature extraction while optimizing the model complexity, achieving a lightweight design tailored for efficient deployment. Subsequently, an unsupervised anomaly detection framework is devised based on scale learning. This framework employs feature dimension transformation and generates efficient supervised signals for distribution alignment learning. This surrogate task enables the framework to adeptly capture and characterize the feature distribution of healthy samples. By gauging the consistency between the input data and the learned model, we precisely quantify the anomaly level of each instance, effectively executing anomaly detection. This approach not only bolsters the accuracy of anomaly detection but also enhances the model’s adaptability to intricate data distributions. Through experimentation on a genuine EL data set, our proposed framework demonstrates pronounced advantages. Comparative to the alternative machine learning or deep learning-based methods, its performance is notable. This accomplishment is poised to furnish robust support for practical applications in the PV panel anomaly detection within the industry.