Comprehensive multiclass debris detection for solar panel maintenance using ANN models

Abstract

Solar photovoltaic (PV) technology has emerged as a leading renewable energy solution globally. However, maintaining optimal performance remains a challenge due to the accumulation of debris, including dust, bird droppings, and other contaminants on the panels. These deposits significantly reduce the efficiency of solar panels, necessitating regular monitoring and cleaning. Automated inspection systems provide a cost-effective alternative to traditional methods by minimizing labor-intensive efforts. This study proposes a machine learning-based framework for detecting and classifying several types of debris on solar panels. The methodology utilizes gray-level co-occurrence matrix (GLCM) texture features and key statistical features extracted from RGB, HSV, and LAB color spaces. A dataset comprising 19 distinct classes, such as “Without Dust,” “Bird Droppings,” “Black Soil,” and “Sand,” was employed to train and evaluate the models. Among the tested classification techniques, artificial neural networks (ANN) achieved a notable accuracy of 93.94%, demonstrating their effectiveness in identifying and categorizing debris. This work underscores the potential of machine learning-based feature extraction and classification techniques to automate solar panel inspection and facilitate targeted cleaning interventions, thereby enhancing overall system efficiency.