Power Plant Gas Turbine Control Optimization
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Solution Overview
Problem
Power plant operators face challenges in maximizing economic return due to the complexity of modern power plants with multiple generating units, as conventional control systems struggle to efficiently manage variability in operating conditions and machine degradation, leading to inefficient operation and suboptimal utilization.
Innovation Solution
A method for optimizing power generation by receiving current state data from gas turbines, defining competing operating modes, predicting performance parameters, determining a cost function, and comparing projected costs to select the most economical operating mode, using a system that includes a plant controller and optimizer to simulate and adjust operations based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control systems are used to manage power plant operations, then system simplicity is maintained, but operational efficiency and economic return deteriorate due to inability to handle variability and machine degradation
Solution Approach 1:
The control system transitions from static to dynamic operation by continuously adjusting control parameters based on real-time plant state data, predicted performance parameters, and varying operating conditions. This enables the system to adapt to machine degradation and external factors, resolving the contradiction between maintaining simplicity and achieving operational efficiency.
Solution Approach 2:
The system implements feedback mechanisms by receiving current state data from sensors, comparing actual performance against predicted performance parameters, and adjusting control inputs accordingly. This closed-loop approach enables continuous optimization of operational efficiency while managing system complexity through structured information flow.
2Adaptability or versatility
If static control profiles are used, then control system simplicity is maintained, but adaptability to changing operating conditions and machine degradation deteriorates
Solution Approach 1:
The system replaces static control profiles with dynamic control strategies that continuously adapt to changing operating conditions and machine degradation states. Control parameters are adjusted in real-time based on predicted performance parameters and current plant state, enabling high adaptability while managing complexity through systematic approaches.
Solution Approach 2:
The system performs preliminary actions by predicting future performance parameters based on current degradation trends and operating conditions. This allows the control system to proactively adjust operations before significant efficiency losses occur, enhancing adaptability while maintaining structured control logic.
3Manufacturing precision
If frequent retuning of control systems is performed to account for degradation, then operational precision is improved, but time loss and operational disruption increase
Solution Approach 1:
The system uses continuous feedback from plant state sensors and predicted performance parameters to maintain control precision without requiring frequent manual retuning. The automated feedback loop continuously adapts control parameters based on actual plant conditions, achieving high precision while eliminating time-consuming manual intervention.
Solution Approach 2:
The control system performs self-service by automatically adjusting its own parameters based on predicted performance degradation and current operating conditions. This eliminates the need for external retuning operations, maintaining control precision while preventing time loss associated with manual system adjustments.
4Reliability
If conservative operation is adopted to preemptively accommodate component deterioration, then component reliability is improved, but productivity and economic return deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting future component degradation trends and planning maintenance activities in advance. This allows the system to operate at optimal levels longer before maintenance is required, improving both reliability through proactive maintenance and productivity by avoiding premature conservative operation.
Solution Approach 2:
The system dynamically adjusts operational parameters based on real-time degradation monitoring and predicted component life. This enables the system to maintain high productivity while managing component reliability through data-driven decisions rather than conservative fixed limits, optimizing the trade-off between output and reliability.
Data Source
AI summary
A method for optimizing a generation of an output level over a selected operating period by a power block, wherein the power block comprises multiple gas turbines for collectively generating the output level. The control method may include: receiving current state data regarding measured operating parameters for each of the gas turbines of the power block; based on the current state data, defining competing operating modes for the power block; based on each of the competing operating modes, deriving a predicted value for a performance parameter regarding the operation of the power block over the selected operating period; determining a cost function and, pursuant thereto, evaluating the operation of the power block based on the predicted value of the performance parameter so to determine a projected cost; and comparing the projected costs from each of the optimized operating modes so to select therefrom an optimized operating mode.