Wind Turbine Blade Bird Collision Detection System
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Solution Overview
Problem
Current methods for monitoring bird and bat collisions with wind turbines are inadequate, particularly for offshore facilities, as they rely on costly carcass surveys and visual observations, which are inaccurate and impractical, and lack efficient means for real-time collision event detection.
Innovation Solution
A multisensory system incorporating vibration sensors, cameras, and machine learning algorithms to automatically detect bird collisions by analyzing blade vibrations and visual data, providing real-time impact detection and reducing data archiving volume through an event-driven trigger architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional carcass survey methods are used to monitor bird and bat collisions, then collision data can be collected, but the methods are costly, inaccurate, and impractical for offshore facilities
Solution Approach 1:
The patent replaces manual mechanical carcass survey methods with an automated sensor-based detection system. Vibration sensors mounted on turbine blades detect collision-induced vibrations, while cameras capture visual evidence. This substitution eliminates the need for manual field surveys and provides continuous, automated collision monitoring with higher accuracy and reduced operational complexity.
Solution Approach 2:
The patent introduces vibration sensors and cameras as intermediary detection devices between the collision event and the monitoring system. These sensors act as mediators that convert physical collision events into detectable signals (vibrations and images), enabling indirect but accurate observation of collisions without requiring direct human intervention or physical retrieval of carcasses.
2Measurement precision
If continuous monitoring of all blade vibrations is performed, then collision detection accuracy is improved, but data archiving volume increases significantly
Solution Approach 1:
The patent extracts and isolates only the relevant collision-related vibration signals from the continuous blade vibration data stream. By using vibration sensors to detect specific collision-induced vibration patterns and triggering camera capture only during detected collision events, the system extracts meaningful data while discarding irrelevant continuous monitoring data, thereby significantly reducing archiving volume while maintaining detection accuracy.
Solution Approach 2:
The patent implements preliminary vibration monitoring that continuously analyzes blade vibrations to detect collision signatures before triggering full data capture. This preliminary detection layer filters out normal operational vibrations and only initiates detailed recording when a collision is detected, enabling accurate collision detection while minimizing unnecessary data storage.
3Reliability
If manual review of collision data is performed, then data accuracy can be verified, but the process is time-consuming and reduces productivity
Solution Approach 1:
The patent implements self-service collision detection and verification through automated sensor systems. The vibration sensors and cameras automatically detect, record, and identify collision events without requiring manual intervention. The system self-verifies collisions through multiple sensor corroboration (vibration patterns plus visual confirmation), eliminating the need for time-consuming manual data review while maintaining high reliability through redundant automated verification mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects bird collisions with high accuracy, reducing the need for manual review and improving the reliability of collision data, enabling more efficient monitoring and potential reduction in wildlife impact.
Implementation Method 1
one or more sensors to continuously sense vibration of the blade
Data Source
AI summary
A multisensory system provides both temporal and spatial coverage capacities for auto-detection of bird collision events. The system includes an apparatus having a first circuitry to capture and store a series of images or video of a blade of a wind turbine; and a memory to store the images from the first circuitry. The apparatus also has one or more sensors to continuously sense vibration of the blade or for acoustic recordings; and a second circuitry to analyze the sensor data stream and/or the series of images or video to identify a cause of the vibration and to trigger the camera(s). A communication interface transmits data from the second circuitry to another device, wherein the second circuitry applies artificial intelligence or machine learning to control sensitivity of the one or more sensors.