Wind Farm Frequency Control Using Dynamic Mode Decomposition
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Solution Overview
Problem
Current wind power participation in primary frequency modulation of the grid faces challenges due to the difficulty in accurately modeling wind turbine dynamics, especially with nonlinear features, and the need for frequent parameter measurement and maintenance.
Innovation Solution
A data-driven wind farm frequency optimization control method based on dynamic mode decomposition, which includes a wind turbine dynamic mode decomposition module and a wind farm frequency optimization control module, allowing for real-time measurement, decomposition, and control of wind turbine states to generate active frequency control instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-driven control policy is used to build wind turbine dynamic model, then control accuracy can be improved, but device complexity and maintenance burden increase due to need for measuring and maintaining numerous device parameters
Solution Approach 1:
The patent replaces the traditional model-driven control approach (which relies on mechanical measurement and maintenance of numerous device parameters) with a data-driven dynamic mode decomposition approach. This substitutes the complex parameter measurement system with a computational method that extracts dynamic characteristics directly from operational data, thereby reducing device complexity and maintenance burden while maintaining control accuracy.
Solution Approach 2:
The wind turbine system uses its own operational data to build and update its dynamic model through dynamic mode decomposition. The system self-learns its dynamic characteristics from historical operational data without requiring external parameter measurement and maintenance, enabling the model to adapt and improve automatically over time.
2Ease of manufacture
If segmental linear fitting is used for data-driven modeling, then model building can be simplified, but manufacturing precision deteriorates due to inability to capture nonlinear dynamics accurately
Solution Approach 1:
The patent transforms the modeling approach from segmental linear fitting to dynamic mode decomposition, which changes the fundamental parameters and mathematical framework used. This allows the model to capture nonlinear dynamics by decomposing the system into dynamic modes with varying frequencies and damping ratios, achieving high accuracy while maintaining computational efficiency.
Solution Approach 2:
The dynamic mode decomposition method introduces an additional dimension of analysis by decomposing the system response into modal components in the frequency-damping domain. This dimensional transformation enables accurate representation of nonlinear dynamics while keeping the model structure computationally tractable, overcoming the limitations of segmental linear approaches.
3Adaptability or versatility
If wind power penetration rate is increased, then energy sustainability is improved, but grid frequency stability deteriorates due to reduced equivalent inertia and decoupling of active output and frequency
Solution Approach 1:
The patent implements a feedback-based primary frequency modulation control system that continuously monitors grid frequency and adjusts wind turbine active power output accordingly. The dynamic mode decomposition model predicts the relationship between control inputs and frequency response, enabling real-time feedback control that stabilizes grid frequency even with high wind power penetration and reduced system inertia.
Solution Approach 2:
The system performs preliminary action by using dynamic mode decomposition to pre-characterize the wind turbine's frequency response characteristics. This allows the control system to anticipate frequency deviations and proactively adjust power output before significant frequency instability occurs, maintaining grid stability proactively rather than reactively.
Data Source
AI summary
A data-driven wind farm frequency control method based on dynamic mode decomposition. The method enables a low-dimension nonlinear dynamic feature of a wind power system to perform global capturing in a high-dimension space through a state transition matrix given by a Koopman operator theory, thus fewer data samples are necessary while control requirements are satisfied with respect to a model fitting accuracy. Meanwhile, a pure linear feature of a control model also provides a favorable foundation for fast on-line dynamic response, thereby satisfying response accuracy and speed requirements simultaneously in an actual control step.