A Novel Ensemble CNN Framework With Weighted Feature Fusion for Fault Diagnosis of Photovoltaic Modules Using Thermography Images

Abstract

The global increase in the adoption of photovoltaic (PV) energy accentuates the imperative of maintaining system efficiency amidst environmental variabilities and faults. The processes of identifying, classifying, and rectifying defects are critical for ensuring the long-term sustainability and performance integrity of PV installations. This article introduces an innovative ensemble convolutional neural network (CNN) model that employs weighted feature fusion to enhance accuracy beyond what is achievable with a singular CNN architecture. By utilizing three proficient CNNs—VGG16, ResNet, and MobileNet—the fusion of deep features extracted from the last layers of these networks’ augments performance, while also capitalizing on the integration of data from multiple CNNs with distinct configurations. This methodology was applied to a publicly available infrared thermography imaging dataset, which includes 12 distinct defects. The proposed models have been subsequently trained, validated, and tested on this dataset. The outcomes indicate a substantial enhancement in the accuracy of defect classification compared to individual CNN models, with an average accuracy of 96%. This approach underscores its utility in defect identification, particularly demonstrating the capacity of the ensemble CNN to classify defects with high precision

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