# Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning

## TL;DR

This paper presents a deep learning-based method for automatic inspection of large-scale solar power plants using drone footage, offering a cost-effective and reliable alternative to traditional manual monitoring methods.

## Contribution

The paper introduces a novel application of deep learning for automated drone-based inspection of utility-scale solar farms, improving efficiency and reducing costs.

## Key findings

- Deep learning can accurately identify module defects from drone footage.
- Automated inspection significantly reduces monitoring costs.
- Method enhances early defect detection, potentially saving large amounts of energy.

## Abstract

Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.