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

VSEngineering 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

Engineering Contradiction:
Improveoperational parameter accuracyVSAvoiddowntime for manual tuning
Core Design Contradiction:
Measurement precisionVSLoss of time

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

Inventive Principle:
Principle #25Self-service

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

Inventive Principle:
Principle #23Feedback

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

Engineering Contradiction:
Improvescheduling algorithm accuracyVSAvoidmodel adjustment system complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

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

Inventive Principle:
Principle #24Intermediary (Mediator)

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

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

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

Engineering Contradiction:
Improvecontinuous operation capabilityVSAvoidoperational parameter accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

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

Inventive Principle:
Principle #20Continuity of useful action

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

Inventive Principle:
Principle #15Dynamics

Data Source

PatentEP2570616B1System and method for simulating a gas turbine compressor
Publication Date: 2021.12.29 GENERAL ELECTRIC CO
  • EP2570616B1 patent drawingFigure 1
  • EP2570616B1 patent drawingFigure 2
  • EP2570616B1 patent drawingFigure 3

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.