Wind Farm Production Forecasting via Meteorological Data Fusion
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Solution Overview
Problem
Variations in wind speed and direction cause fluctuations in electric power output from wind farms, leading to deficiencies in grid power delivery and potential disconnection from the grid, which affects both local and regional energy distribution and economic stability.
Innovation Solution
A method and system for generating wind turbine generator availability data, wide-area meteorological data, and narrow-area meteorological forecasts, using algorithms to manipulate these data sets for accurate electric power production forecasting, ensuring reliable and efficient wind farm operation by predicting power output and adjusting for site-specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If wind farms are integrated into the electric power system to generate electricity, then electric power production is achieved, but variations in wind speed and direction cause fluctuations in power output leading to grid frequency variations and power delivery deficiencies
Solution Approach 1:
The system performs preliminary actions by generating meteorological forecasts and WTG availability predictions in advance of actual power generation. Multiple forecast algorithms process data ahead of time to predict future power output, allowing the grid to prepare for expected variations and maintain frequency stability through proactive scheduling and resource allocation.
2Productivity
If multiple wind turbine generators are congregated in a wind farm to increase power output, then electric power production increases, but the variability of individual turbines amplifies overall power delivery fluctuations
Solution Approach 1:
The system implements feedback by continuously monitoring actual power output from multiple WTGs and comparing it against forecasted values. The forecast algorithms are refined based on performance data and weather pattern analysis, creating a feedback loop that improves prediction accuracy over time and enables better coordination of turbine operations to smooth power delivery variations.
Solution Approach 2:
The system merges data from multiple sources including wide-area meteorological forecasts, narrow-area local conditions, and individual WTG availability status. By combining these diverse data streams through integrated algorithms, the system creates a comprehensive view of expected power output that accounts for both environmental factors and equipment status, enabling more stable aggregate power delivery.
3Area of stationary object
If wide-area meteorological data is used for forecasting, then broad regional trends are captured, but site-specific local conditions are not adequately represented
Solution Approach 1:
The system segments the forecasting approach by dividing it into two distinct levels: wide-area meteorological forecasting for broad regional trends and narrow-area local condition analysis for site-specific accuracy. Each level operates with appropriate algorithms and data resolution, and the results are integrated to provide both comprehensive coverage and precise local predictions for individual wind farms and turbines.
Data Source
AI summary
A method of operating a wind farm that includes a plurality of wind turbine generators (WTGs) includes generating wind turbine generator (WTG) availability data for each WTG. The method also includes generating wide-area meteorological data for a first geographical region. The method further includes generating narrow-area meteorological forecast data for a second geographical region by transmitting at least a portion of the wide-area meteorological data to at least one resident narrow-area meteorological forecast algorithm. The first geographical region includes at least a portion of the second geographical region. The method also includes generating electric power production forecast data by using at least one resident electric power production forecast algorithm to manipulate the WTG availability data and the narrow-area meteorological forecast data.