Wind Park AI Control for Early Turbine Shutdown Coordination
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Solution Overview
Problem
Wind turbines in a wind park may experience unpredictable extreme weather events, leading to uneven shutdowns and potential damage due to fast-changing weather conditions, with some turbines continuing to operate until they detect alarm states, while others have already shut down.
Innovation Solution
A method using a trained AI model to analyze incident signal data from multiple wind turbines, identify patterns, and associate appropriate actions, enabling proactive control of wind turbines by comparing detected signals to identified patterns to perform protective actions before critical conditions are reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If safety systems automatically shut down wind turbines when design parameters are exceeded, then wind turbine protection is improved, but response time to extreme weather events deteriorates due to detection delays
Solution Approach 1:
The system performs preliminary actions by analyzing patterns from multiple wind turbines to predict extreme weather events before they affect individual turbines. The AI model identifies early warning signs from aggregated data, enabling proactive shutdown decisions that occur before traditional safety systems would detect parameter exceedances, thus reducing response time while maintaining protection reliability
Solution Approach 2:
The system implements feedback by continuously collecting incident signal data from multiple wind turbines and using the AI model to analyze patterns in real-time. This feedback loop enables the system to learn from aggregated turbine responses and improve prediction accuracy, allowing earlier detection and response to extreme weather events while maintaining reliable turbine protection
2Device complexity
If wind turbines operate independently with individual safety systems, then system simplicity is improved, but coordination during extreme weather events deteriorates leading to uneven shutdowns and potential damage
Solution Approach 1:
The system merges data from multiple independent wind turbine safety systems into a centralized AI analysis platform. By combining incident signal data from numerous turbines, the system creates a collective intelligence that identifies extreme weather patterns earlier and coordinates shutdown actions across the wind park, improving reliability without significantly increasing individual turbine complexity
Solution Approach 2:
The AI model acts as an intermediary between individual wind turbine safety systems. It receives incident signal data from independent turbines, analyzes patterns, and generates coordinated control decisions that are sent back to individual turbines. This intermediary layer enables synchronized response to extreme weather events while preserving the simplicity of individual turbine systems
Data Source
AI summary
A method for controlling wind turbines. Incident signal data is obtained from wind turbines and fed to an artificial intelligence (AI) model in order to identify patterns in the incident signals generated by the wind turbines. One or more actions are associated to the identified patterns, based on identified actions performed by the wind turbines in response to the generated incident signals. During operation of the wind turbines, one or more incident signals from one or more wind turbines are detected and compared to patterns identified by the AI model. In the case that the detected incident signal(s) match(es) at least one of the identified patterns, the wind turbine(s) are controlled by performing the action(s) associated with the matching pattern(s).