Adaptive Turbine Model for Real-Time Control Strategy Comparison
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Solution Overview
Problem
Turbine control systems face challenges in maintaining efficient operation due to component degradation and varying operating conditions, leading to divergence from desired states, and there is a need for a method to compare and adjust control strategies in real-time.
Innovation Solution
Implementing an alternate adaptive turbine model that uses a Kalman filter to tune and compare actual and alternate control strategies, allowing for real-time adjustments and analysis of operational differences, enabling efficient operation and predicting performance under different control strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the turbine operates based on a legacy control system, then the control logic is simple and well-understood, but operational efficiency is lower compared to upgraded control systems
Solution Approach 1:
The second adaptive model creates a virtual copy of the turbine's operation under alternate control strategies. By tuning this model to reflect actual turbine conditions and simulating how the turbine would perform under different control logic, the system can quantify efficiency gains without physically switching control systems
Solution Approach 2:
The system uses feedback from the actual turbine operation (through the Kalman filter tuning process) to continuously update both adaptive models. This allows comparison of actual performance with simulated performance under alternate strategies, providing quantifiable feedback on potential efficiency improvements
2Ease of operation
If control scheduling algorithms use assumed turbine conditions, then the control logic is straightforward, but the turbine operates increasingly away from desired states as components degrade
Solution Approach 1:
The adaptive models dynamically adjust to changing turbine conditions through continuous tuning using the Kalman filter. As components degrade and operating conditions change, the models automatically update their parameters to reflect actual turbine state, maintaining accuracy without requiring manual recalibration or complex control logic changes
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time tuning and comparison of control strategies, improving operational efficiency and enabling the identification of efficiency gains by analyzing the turbine's performance under different control systems, thus optimizing turbine operation.
Implementation Method 1
The model may be tuned using a Kalman-type filter that processes measurements and model outputs to determine tuning factors that adjust the model.
Data Source
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AI summary
A method for controlling and modeling a turbine (10) is provided. The method may include modeling (510) the turbine (10) by a primary adaptive turbine model (310) that includes at least one primary operating parameter (330) and modeling (530) the turbine (10) by an alternate adaptive turbine model (320) that includes at least one alternate operating parameter (360). The method may also include determining (525) a first output value from the primary adaptive turbine model (310) that corresponds at least in part to the operation of the turbine (10) based on a primary control strategy and adjusting the alternate operating parameter (360) or parameters based on an alternate control strategy and based at least in part on the first output value. The method may further include determining (575) comparison data based at least in part on a comparison between the primary control strategy and the alternate control strategy.