Adaptive Compressor Simulation for Gas Turbine Model Tuning
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Solution Overview
Problem
Existing gas turbine control systems struggle to maintain accuracy over extended operation due to component efficiency degradation and flow capacity changes, leading to divergence from desired operational states, requiring frequent manual tuning and downtime.
Innovation Solution
An adaptive gas turbine model using a Kalman filter with a model sensitivity matrix and Kalman filter gain matrix to automatically adjust and tune the compressor simulation, accounting for changes in efficiency and flow capacity in real-time, ensuring accurate prediction and control of operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning is performed to correct efficiency degradation and flow capacity changes, then the gas turbine can be adjusted to desired operational state, but the gas turbine must be taken off-line requiring downtime
Solution Approach 1:
The system performs self-tuning by automatically comparing measured operating parameters against the mathematical model and generating correction factors through a Kalman filter gain matrix, eliminating the need for manual intervention and keeping the gas turbine online throughout the tuning process
Solution Approach 2:
The system continuously monitors measured operating parameters and feeds this information back to the mathematical model, using the differences between measured and modeled parameters to generate real-time correction factors that adjust the model and maintain accuracy without taking the system offline
2Reliability
If the mathematical model is adjusted to account for component efficiency degradation and flow capacity changes, then the scheduling algorithms remain accurate, but the system complexity increases
Solution Approach 1:
The Kalman filter gain matrix serves as an intermediary that automatically translates differences between measured and modeled operating parameters into correction factors, simplifying the adjustment process while maintaining high accuracy without requiring complex manual intervention systems
Solution Approach 2:
The system replaces manual mechanical adjustment procedures with an automated computational approach using Kalman filter algorithms, substituting human operator actions with mathematical computations that continuously adjust the model based on real-time data
3Productivity
If the gas turbine operates for extended periods without tuning, then productivity is maintained, but component efficiency degradation causes operational parameters to diverge from desired states
Solution Approach 1:
The system enables continuous tuning operations without interrupting gas turbine productivity by performing model adjustments in real-time during normal operation, eliminating the need to stop production for maintenance tuning activities
Solution Approach 2:
The mathematical model is made dynamic and adaptive, automatically adjusting to component efficiency degradation and flow capacity changes during extended operation through continuous correction factor generation, allowing the system to maintain accuracy while operating continuously without fixed scheduling intervals
Data Source
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AI summary
A method (100) for simulating a compressor of a gas turbine is disclosed. The method may generally include determining (102) a predicted pressure ratio and a predicted mass flow of the compressor based on a model of the gas turbine, monitoring (104) an actual pressure ratio and an actual mass flow of the compressor, determining (106) difference values between at least one of the predicted pressure ratio and the actual pressure ratio and the predicted mass flow and the actual mass flow, modifying (108) the difference values using an error correction system to generate a compressor flow modifier and using (110) the compressor flow modifier to adjust the predicted pressure ratio and the predicted mass flow. And a corresponding system for simulating a compressor (12) of a gas turbine engine.