Distributed Intelligent System Architecture for UAV-Assisted Monitoring of Wind Energy Infrastructure

TL;DR

This paper introduces a distributed intelligent system architecture utilizing UAVs with visual and thermal sensors for efficient, real-time wind turbine defect detection, significantly improving accuracy and reducing inspection time.

Contribution

The paper presents a novel distributed system architecture that enhances UAV-based wind turbine inspection accuracy and efficiency with advanced algorithms and real-time data processing.

Findings

Detection accuracy up to 94%

Inspection time reduced to 1.5 hours per turbine

Scalable and reliable maintenance solution

Abstract

With the rapid development of green energy, the efficiency and reliability of wind turbines are key to sustainable renewable energy production. For that reason, this paper presents a novel intelligent system architecture designed for the dynamic collection and real-time processing of visual data to detect defects in wind turbines. The system employs advanced algorithms within a distributed framework to enhance inspection accuracy and efficiency using unmanned aerial vehicles (UAVs) with integrated visual and thermal sensors. An experimental study conducted at the "Staryi Sambir-1" wind power plant in Ukraine demonstrates the system's effectiveness, showing a significant improvement in defect detection accuracy (up to 94%) and a reduction in inspection time per turbine (down to 1.5 hours) compared to traditional methods. The results show that the proposed intelligent system architecture provides a scalable and reliable solution for wind turbine maintenance, contributing to the durability and performance of renewable energy infrastructure.