Distributed Wind Turbine Data Analysis Across Multiple Power Plants
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Solution Overview
Problem
SCADA systems for wind turbines are limited in their ability to analyze data from multiple wind power plants, leading to localized operational parameter adjustments that do not account for conditions across similar models of turbines, preventing aggregation of operational data for manufacturer recommendations.
Innovation Solution
A distributed data analysis system is implemented, structured into zones (wind power plant, regional, and central) to collect and analyze data from multiple wind power plants, allowing for the aggregation of operational data across different operators and sites, enabling optimized performance adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a SCADA system is used for local data analysis at a single wind power plant, then the system complexity is low and ease of operation is maintained, but the data analysis scope is limited and cannot aggregate data from multiple wind power plants
Solution Approach 1:
The system is divided into multiple zones (wind power plant zones, regional zones, and central zones) that can independently analyze data at their respective levels. Each zone operates autonomously but contributes to the overall distributed analysis network, enabling scalable data aggregation without requiring a single complex centralized system.
Solution Approach 2:
The patent introduces a hierarchical spatial dimension to data analysis by organizing zones in a multi-level structure. This vertical dimensionality allows local SCADA systems to maintain simplicity while enabling global data aggregation through the hierarchical architecture, resolving the contradiction between local simplicity and global versatility.
2Productivity
If data analysis is performed locally at each wind power plant, then the response time is fast and operational control is immediate, but the operational parameter adjustments cannot account for conditions across similar turbine models at other plants
Solution Approach 1:
The distributed data analysis system implements multi-level feedback mechanisms where analysis results from regional and central zones are fed back to local wind power plant zones. This feedback loop enables local SCADA systems to incorporate broader operational patterns and insights from other plants, improving the completeness of operational information while maintaining fast local response times.
Solution Approach 2:
The system performs preliminary data aggregation and analysis at regional and central zones before results are applied at local levels. This preliminary action allows comprehensive data processing to occur in advance, ensuring that when local adjustments are made, they are informed by complete information from across the entire network, thus preventing information loss.
3Loss of information
If a centralized data analysis system is implemented to aggregate data from multiple wind power plants, then data aggregation capability is improved, but the system complexity increases and data transmission requirements increase
Solution Approach 1:
Rather than implementing a single centralized data analysis system, the patent segments the analysis function across multiple distributed zones. Each zone performs data aggregation within its regional scope, eliminating the need for a monolithic centralized system and reducing overall system complexity while maintaining comprehensive data aggregation capabilities.
Solution Approach 2:
The system merges multiple distributed data analysis nodes into a coordinated network that functions as a unified system. By combining the capabilities of regional and central zones, the system achieves comprehensive data aggregation without the complexity of a single centralized architecture, as each node contributes to the collective intelligence of the network.
4Loss of information
If data is transmitted from local SCADA systems to regional and central zones, then aggregated operational data becomes available for manufacturer recommendations, but data transmission infrastructure and network requirements increase
Solution Approach 1:
The system extracts only the necessary operational data from local SCADA systems for transmission to regional and central zones, rather than transmitting all raw data. This selective extraction reduces the volume of transmitted data while ensuring that manufacturer-relevant information is captured and aggregated at higher levels of the hierarchy.
Data Source
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AI summary
A method, controller, wind turbine, and computer program product are disclosed for processing and analyzing wind turbine operational data in a distributed data analysis system. An example method generally includes obtaining, at a first data analysis system, operational data from a plurality of wind turbines in a first wind power plant, determining adjustments to wind turbine operational parameters based on the operational data, pushing the adjustments to the first wind power plant, transmitting the operational data to a second data analysis system not located at the first wind power plant, and transmitting the operational data from the first wind power plant and atleast a second wind power plant to a third data analysis system, wherein the third data analysis system comprises a global data analysis system.