MetOcean Data
Preventing Projects Delay, Cost Overrun and Revenue Losses
Metocean Data Management
Metocean data management and analysis are crucial for understanding site conditions, optimizing design, and minimizing risks in offshore wind projects.
This process involves collecting, validating, processing, analyzing, and maintaining extensive datasets.
1.
Data Collection and Acquisition
- In-situ Data Collection: Hindcast systems. Deploy Management of sensors such as metocean buoys, wave-rider buoys, LiDAR, and met masts to collect on-site data on wind, wave, and current characteristics.
- Satellite and Remote Sensing Data: Use satellite and radar sources to gather large-scale oceanographic and atmospheric data, especially for wave and wind patterns.
- Historical and Regional Data Acquisition: Obtain historical data from nearby projects, government agencies, or marine data providers to build a comprehensive baseline.
- Real-Time Monitoring Setup: Implement real-time data transmission from sensors to databases, allowing for continuous monitoring and immediate access to data.
- Others.
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2.
Statistical Analysis and Modeling
- Extreme Value Analysis: Perform statistical analysis to model extreme weather events, such as maximum wave heights, currents, and wind speeds, which are critical for design and risk assessments.
- Probability Distributions for Metocean Parameters: Develop probability distributions for wind, wave, and current speeds, factoring in seasonal and diurnal variability.
- Environmental Load Modeling: Use metocean data to calculate loads on turbines, foundations, and cables, providing essential input for structural design.
- Weather Window and Downtime Analysis: Analyze weather windows for safe installation and maintenance, identifying optimal timing to reduce weather-related downtime.
- Others.
3.
Ongoing Monitoring and Predictive Analytics
- Real-Time Data Monitoring and Alerts: Monitor data in real-time with alert systems for extreme conditions, allowing for proactive measures during project operations.
- Machine Learning and Predictive Modeling: Apply machine learning algorithms to predict upcoming conditions, helping optimize maintenance schedules and operations planning.
- Trend Analysis for Long-Term Changes: Continuously analyze long-term trends in metocean data to identify any significant changes that might impact project performance or durability.
- Others.
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