Edge-Intelligent Photovoltaic Fault Localization via NAS-Optimized Feature-Space Sub-Pixel Matching

The rapid deployment of Industrial Internet of Things (IIoT) systems, such as large-scale photovoltaic (PV) power stations in modern power grids, has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints. In this work, we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization. The framework adopts a modular, edge-oriented design in which a radial basis function (RBF) network is first employed as a lightweight screening module to enable conditional execution, thereby reducing unnecessary computation for non-faulty samples. For suspicious samples, a compact convolutional feature extractor is activated to generate discriminative representations. The architecture of this feature extractor is automatically optimized using neural architecture search (NAS) in an offline design stage, explicitly balancing localization accuracy and computational efficiency for industrial edge hardware. Sub-pixel displacement estimation and recursive partitioning are then performed in the learned feature space using a sum of squared differences-based, preserving the mathematical transparency of classical sub-pixel matching while significantly improving robustness to thermal noise and background interference. Unlike large end-to-end detection models, the proposed framework combines intelligent feature representation with interpretable localization mechanisms, resulting in a flexible and resource-efficient solution for edge deployment. Experimental results on a photovoltaic infrared fault image dataset demonstrate that the proposed NAS-optimized feature-space sub-pixel matching framework achieves more stable fault localization than other baselines, with only marginal additional computational overhead.