Presentations

Abstract

The increasing global deployment of wind energy infrastructure has intensified concerns about wildlife conservation, particularly regarding bird and bat fatalities through turbine collisions. This study presents an innovative approach using high-resolution thermal imaging technology to address this critical challenge while optimizing wind farm operations. Our research introduces a sophisticated detection and tracking system that effectively monitors avian and chiropteran activity in the vicinity of wind turbines, enabling selective curtailment decisions based on real-time risk assessment.     The system employs state-of-the-art thermal cameras strategically positioned around wind turbines, providing continuous 360-degree surveillance. These cameras capture detailed heat signatures with exceptional sensitivity, enabling reliable birds and bats detection at significant distances, even in challenging environmental conditions such as complete darkness or light precipitation. Our novel image processing algorithm, has been developed through deep learning techniques and trained on an extensive dataset of thermal signatures from various bird and bat species.    Unlike traditional acoustic monitoring systems commonly used for bat detection, thermal imaging remains unaffected by wind-induced noise or mechanical interference from turbine operations. The system's robust performance across varying weather conditions and seasons represents a significant advancement over existing wildlife monitoring methods. Through extensive field testing across multiple wind farm sites in diverse geographical locations, we validated the technology's reliability in different environmental contexts and with various species compositions.     The implementation of our thermal detection system has demonstrated substantial benefits for both wildlife protection and wind farm operations. Limiting production losses through optimized curtailment strategies based on real-time data and risk assessment, achieves a significant improvement over conventional time-based curtailment approaches.     Our findings also reveal important patterns in wildlife behavior near wind turbines, contributing valuable data to the scientific understanding of species-specific risk factors and movement patterns. The system's ability to collect continuous, high-quality data enables the development of predictive models for wildlife activity, further enhancing the efficiency of curtailment decisions.     This research demonstrates that advanced thermal imaging technology, combined with sophisticated data analysis, can effectively reconcile the competing demands of renewable energy production and wildlife conservation. The system's proven ability to minimize both wildlife fatalities and unnecessary curtailment presents a state of the art solution for the wind energy industry's sustainable growth and AEP maximization.