Improving UAV-Based Monitoring of Solar Power Plants Using Coverage Path Planning Model with Adaptive Learning Algorithm
Hamid Sayyadi, Mahmood Mohassel Feghhi, Mahdi Nangir, Javad Sayyadi · 2025
Solar power plants, as one of the key renewable energy sources, require advanced solutions for effective monitoring and enhanced efficiency. This paper introduces a novel Coverage Path Planning (CPP-ALA) algorithm for monitoring solar power plants using UAVs, designed to improve energy efficiency and maintenance operations by employing real-time image processing, adaptive learning, and deep learning techniques. The proposed algorithm utilizes dynamic adaptive learning rates, a batch size of 16, and the Adam optimizer, combined with ReLU and Sigmoid activation functions, for the detection of defects in solar panels. Experiments conducted on UAV-acquired aerial images of solar power plants evaluated the performance of CPP-ALA against three other methods: semantic segmentation-based coverage planning, multi-agent reinforcement learning (MARL), and wavefront coverage planning. The results demonstrated that CPP-ALA achieved 97.8 % defect detection accuracy, a processing time of 1.8 seconds per image, and a localization accuracy of$\pm 5$pixels. Moreover, the algorithm exhibited a 30% reduction in computational complexity, enabling real-time implementation. This approach establishes a new standard in UAV coverage path planning and defect detection, providing an efficient solution for solar power plant inspection and supporting renewable energy applications.