Dual-Imager Avian Detection for Wind Turbine Strike Prevention
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Solution Overview
Problem
Existing avian detection systems are inadequate in providing reliable, cost-effective, and efficient detection of flying avians over large areas, particularly in distinguishing between different species and reducing false positives, which is crucial for wind turbine safety and environmental impact assessment.
Innovation Solution
A system comprising a first wide field of view imager and a second high zoom imager, along with efficient algorithms, provides substantially complete hemispherical coverage for detecting avians up to 1.2 km, using pattern recognition and edge detection to identify avians of interest and minimize false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If radar systems are used for avian detection, then detection range is improved, but the ability to distinguish between avian species deteriorates
Solution Approach 1:
The patent replaces radar systems with optical imaging systems (cameras) that capture visual information. This substitution enables the system to distinguish between different avian species through visual recognition of morphological features such as wing shape, body size, and flight patterns, while maintaining effective detection ranges through strategic placement of multiple cameras and image processing algorithms.
2Area of stationary object
If radar systems are used for avian detection, then detection coverage is improved, but false positives increase
Solution Approach 1:
The patent replaces radar with optical imaging systems that capture detailed visual data. This enables the system to distinguish between actual avians and false targets (such as turbine blades or debris) through visual recognition, significantly reducing false positives while maintaining comprehensive detection coverage through multiple strategically positioned cameras.
Solution Approach 2:
The system employs image processing algorithms that continuously analyze visual data to confirm avian identity. The system uses feedback loops where detected objects are validated through multiple analysis criteria (morphological features, flight patterns, size) before triggering alerts or shutdown sequences, thereby reducing false positives while maintaining detection coverage.
3Measurement precision
If human-based detection methods are used, then species identification accuracy is improved, but labor intensity and cost increase
Solution Approach 1:
The patent implements automated detection systems using optical cameras and image processing algorithms that independently perform avian detection, identification, and tracking without human intervention. The system self-service capability includes automatic analysis of visual data, recognition of avian species through algorithmic pattern recognition, and automated response to detected avians, thereby eliminating labor-intensive human-based detection while maintaining high species identification accuracy.
Solution Approach 2:
The patent replaces human-based detection with automated optical imaging and computer vision systems. This substitution enables continuous, uninterrupted monitoring with consistent species identification accuracy through algorithmic analysis of visual features, while dramatically improving productivity by eliminating the need for trained human observers.
4Ease of manufacture
If conventional bird strike search methods are used, then cost is reduced, but detection reliability deteriorates
Solution Approach 1:
The patent replaces conventional post-event search methods with real-time optical detection systems. This substitution provides continuous monitoring and reliable detection of avians before potential collisions occur, while keeping costs manageable through the use of standard optical cameras and processed through efficient image analysis algorithms, rather than requiring expensive specialized equipment.
Data Source
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AI summary
Provided herein are detection systems and related for detecting moving objects in an airspace surrounding the detection system. In an aspect, the moving object is a flying animal and the detection system comprises a first imager and a second imager that determines position of the moving object and for moving objects within a user selected distance from the system the system determines whether the moving object is a flying animal, such as a bird or bat. The systems and methods are compatible with wind turbines to identify avian(s) of interest in airspace around wind turbines and, if necessary, take action to minimize avian strike by a wind turbine blade.