AI-Powered Smart Wind Turbine Monitoring for Wildlife Safety & Predictive Maintenance
Overview
The client wanted to enhance wind turbine efficiency and safety by integrating AI-driven computer vision at the edge. They required a scalable solution to detect and track wildlife activity, trigger automated countermeasures, and integrate predictive maintenance insights to minimize downtime, reduce maintenance costs, and ensure regulatory compliance.
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Challenges
Need for real-time detection of birds and other objects around wind turbines to prevent operational risks
Difficulty in automating visual monitoring and triggering proactive countermeasures
Lack of intelligent analytics for predictive maintenance decision-making
Solution
- Deployed ML models on turbine-mounted edge devices for low-latency video processing with reduced bandwidth usage
- Implemented computer vision to detect, classify, and track birds, animals & moving objects near turbine blades and towers
- Enabled automated countermeasures such as blade speed modulation, warning signals, and temporary shutdowns during high-risk wildlife activity
- Integrated predictive maintenance by analyzing video feeds for blade wear, structural anomalies, icing, and collision impacts
- Designed a scalable distributed architecture for multi-turbine deployments with centralized fleet-wide monitoring
- Leveraged historical operational and visual data to retrain AI models, improving detection accuracy and predictive insights
Outcomes
- Reduced wildlife collision risks and environmental impact by enabling automated countermeasures, ensuring regulatory compliance and ecosystem protection
- Enhanced turbine uptime and lowered maintenance costs through predictive insights, minimizing unplanned repairs and downtime while extending equipment lifespan
