Wind Farm Operation Optimization via Concurrent Maintenance Scheduling
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Solution Overview
Problem
Current wind farm maintenance and power production planning are serially approached, with maintenance schedules being inflexible and not concurrently optimized with power production, leading to higher operational costs, especially in offshore wind farms due to weather dependencies and limited component exchange capabilities.
Innovation Solution
A method and system that concurrently optimize maintenance scheduling and power production in wind farms by using a life index or health status model to adapt turbine operation, considering mechanical and electrical stress, wind conditions, and market factors, allowing for flexible energy production and maintenance decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance events are planned in distinct time steps with fixed schedules, then maintenance planning is simplified and equipment reliability is maintained, but operational flexibility is reduced and operational costs increase
Solution Approach 1:
The patent implements dynamic maintenance scheduling where maintenance events are not fixed in time but are scheduled based on real-time component health status and forecasted wind conditions. The optimization model adjusts maintenance timing dynamically to balance reliability requirements with operational flexibility, allowing maintenance to be postponed or advanced based on actual turbine condition and weather forecasts.
Solution Approach 2:
The system performs preliminary assessment of component health status and forecasts future wind conditions before scheduling maintenance. By evaluating the life index trajectory and predicting future stress conditions, the system proactively determines optimal maintenance timing that prevents failures while maximizing energy production, rather than reacting to actual failures or adhering to rigid schedules.
2Reliability
If maintenance schedules are established as unchallengeable constraints, then maintenance execution is ensured, but power production optimization is limited
Solution Approach 1:
The patent merges the maintenance scheduling problem with the power production optimization problem into a single integrated optimization model. Instead of treating maintenance constraints as fixed and separate from production planning, the model simultaneously optimizes both maintenance timing and power production schedules, allowing trade-offs between maintenance execution and energy production based on forecasted wind conditions and component health trajectories.
Solution Approach 2:
The system changes the parameter of maintenance timing from fixed schedule values to optimized variables that can adjust based on wind forecasts and health status. By treating maintenance timing as a flexible parameter rather than a fixed constraint, the model can shift maintenance events to periods with lower wind resource availability, thereby minimizing impact on power production while still ensuring maintenance is performed.
3Reliability
If turbine operation is controlled to comply with present fatigue load requirements, then component design lifetime is respected, but operational flexibility and energy production are reduced
Solution Approach 1:
The system performs preliminary evaluation of the life index trajectory based on forecasted wind conditions and current component health status before making operational decisions. By predicting future stress accumulation, the system can proactively adjust operational parameters to stay within fatigue limits while maximizing energy production, rather than reacting to actual fatigue accumulation or applying conservative fixed limits.
Solution Approach 2:
The patent changes the operational control approach from compliance with fixed fatigue load limits to optimization of operational parameters within fatigue constraints. The model adjusts turbine operation parameters dynamically based on the predicted life index trajectory, allowing maximum energy production while ensuring the component life index remains within acceptable bounds over the planning horizon.
4Reliability
If maintenance is performed more frequently to ensure component reliability, then component failure risk is reduced, but operational costs and downtime increase
Solution Approach 1:
The system performs preliminary assessment of component health status and forecasts future stress conditions to determine the actual maintenance needs. By evaluating the life index trajectory and predicting when maintenance will be required, the system can schedule maintenance proactively at optimal times rather than reacting to failures or following overly conservative fixed schedules, thereby minimizing unnecessary downtime.
Solution Approach 2:
The model incorporates feedback from actual component health status measurements and compares them with predicted trajectories. This feedback mechanism allows the system to adjust maintenance scheduling based on actual component condition rather than fixed intervals, performing maintenance only when and where it is truly needed, thereby reducing unnecessary maintenance downtime and costs.
Data Source
AI summary
The present application is concerned with a flexible way of operating a wind farm with a plurality of degrading wind turbine components. According to the invention, maintenance scheduling and power production in the wind farm are handled concurrently in a single optimization step. Instead of a serial approach first scheduling maintenance activities and subsequently adapting the power production and/or wind turbine operation the two aspects are optimized together. The wind farm operation takes maintenance aspects into account by adapting life index or health status based on modeled mechanical and electrical stress. Accordingly, the wind farm owner may decide when and how much energy to produce accepting which level of stress to the turbine equipment. The proposed optimization of wind farm operation includes all aspects of transmission network operator settings, the topology of wind farms and the underlying collector grid, the short and long term wind conditions forecasts, the conditions of the turbines, the estimated remaining operational time under different usage patterns and times, as well as aspects of the electricity market.