Real Time Prognostic Strategies With Application to Stationary Gas Turbines

2006 ◽  
Author(s):  
Rajat Sekhon ◽  
Hany Bassily ◽  
John Wagner
Keyword(s):  
Real Time ◽  
Gas Turbines ◽  
Health Management ◽  
Sensory Information ◽  
Prognostic Information ◽  
Maintenance Schedule ◽  
Plant Behavior ◽  
Industrial Sectors ◽  
Parallel Fashion ◽  
Transient And Steady State

Stationary gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. The efficiency of these complex multi-domain systems is dependent on the turbine's design, established operating envelope, environmental conditions, and maintenance schedule. A real time health management strategy can enhance overall plant reliability through the continual monitoring of transient and steady-state system operations. The availability of sensory information for control system needs often allow diagnostic/prognostic algorithms to be executed in a parallel fashion which warn of impending system degradations. Specifically, prognostic strategies estimate the future plant behavior which leads to minimized maintenance costs through timely repairs, and hence, improved reliability. In this paper, statistical and wavelet prognostic methods are presented to forecast system health. For the statistical approach, a multi-dimensional empirical description reveals dominant data trends and estimates future behavior. The wavelet approach uses second order Daubechies wavelet coefficients to generate signal approximations that forecast future plant operation. Experimental data has been collected on a Solar Mercury 50 stationary gas turbine. The monitored plant signals were analyzed to identify prognostic information for preventative action recommendations. Representative results are presented and discussed to compare the overall performance of each prognostic algorithm.

Real Time Prognostic Strategies: Application to Gas Turbines

2011 ◽  
Vol 312-315 ◽  
pp. 601-606
Author(s):  
M. Yadegari
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Health Management ◽  
System Behavior ◽  
Industrial Sectors ◽  
Healthy Working ◽  
The World ◽  
Short And Long Term

Gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. A health management system can incorporate prognostic algorithms to effectively interpret and determine the healthy working span of a gas turbine. The research project’s objective is to develop real-time monitoring and prediction algorithms for simple cycle natural gas turbines to forecast short and long term system behavior.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

A Modular Code for Real Time Dynamic Simulation of Gas Turbines in Simulink

2002 ◽  
Vol 128 (3) ◽  
pp. 506-517 ◽  
Author(s):  
S. M. Camporeale ◽  
B. Fortunato ◽  
M. Mastrovito
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Gas Turbine ◽  
Dynamic Behavior ◽  
Gas Turbines ◽  
Simulation Software ◽  
Computational Time ◽  
High Fidelity ◽  
Time Step ◽  
The Mathematical Model

A high-fidelity real-time simulation code based on a lumped, nonlinear representation of gas turbine components is presented. The code is a general-purpose simulation software environment useful for setting up and testing control equipments. The mathematical model and the numerical procedure are specially developed in order to efficiently solve the set of algebraic and ordinary differential equations that describe the dynamic behavior of gas turbine engines. For high-fidelity purposes, the mathematical model takes into account the actual composition of the working gases and the variation of the specific heats with the temperature, including a stage-by-stage model of the air-cooled expansion. The paper presents the model and the adopted solver procedure. The code, developed in Matlab-Simulink using an object-oriented approach, is flexible and can be easily adapted to any kind of plant configuration. Simulation tests of the transients after load rejection have been carried out for a single-shaft heavy-duty gas turbine and a double-shaft aero-derivative industrial engine. Time plots of the main variables that describe the gas turbine dynamic behavior are shown and the results regarding the computational time per time step are discussed.

Innovative Minimally Intrusive Sensor Technology Development for Versatile Affordable Advanced Turbine Engine Combustors

2002 ◽  
Author(s):  
Neil Goldstein ◽  
Carlos A. Arana ◽  
Fritz Bien ◽  
Jamine Lee ◽  
John Gruninger ◽  
...  
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Real Time ◽  
Gas Turbines ◽  
High Performance ◽  
Chemical Species ◽  
Sensor Technology ◽  
Spectral Signature ◽  
Temperature Pattern ◽  
Hot Gas

The feasibility of an innovative minimally intrusive sensor for monitoring the hot gas stream at the turbine inlet in high performance aircraft gas turbine engines was demonstrated. The sensor uses passive fiber-optical probes and a remote readout device to collect and analyze the spatially resolved spectral signature of the hot gas in the combustor/turbine flowpaths. Advanced information processing techniques are used to extract the average temperature, temperature pattern factor, and chemical composition on a sub-second time scale. Temperatures and flame composition were measured in a variety of combustion systems including a high pressure, high temperature combustion cell. Algorithms for real-time temperature measurements were developed and demonstrated. This approach should provide a real-time temperature profile, temperature pattern factor, and chemical species sensing capability for multi-point monitoring of high temperature and high pressure flow at the combustor exit with application as an engine development diagnostic tool, and ultimately, as a real-time active control component for high performance gas turbines.

scholarly journals A Real-Time Degradation Model for Hardware in the Loop Simulation of Fuel Cell Gas Turbine Hybrid Systems

2015 ◽  
Author(s):  
Valentina Zaccaria ◽  
Alberto Traverso ◽  
David Tucker
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Current Density ◽  
Gas Turbines ◽  
Hardware In The Loop ◽  
Fuel Utilization ◽  
Time Operation ◽  
The Impact

The theoretical efficiencies of gas turbine fuel cell hybrid systems make them an ideal technology for the future. Hybrid systems focus on maximizing the utilization of existing energy technologies by combining them. However, one pervasive limitation that prevents the commercialization of such systems is the relatively short lifetime of fuel cells, which is due in part to several degradation mechanisms. In order to improve the lifetime of hybrid systems and to examine long-term stability, a study was conducted to analyze the effects of electrochemical degradation in a solid oxide fuel cell (SOFC) model. The SOFC model was developed for hardware-in-the-loop simulation with the constraint of real-time operation for coupling with turbomachinery and other system components. To minimize the computational burden, algebraic functions were fit to empirical relationships between degradation and key process variables: current density, fuel utilization, and temperature. Previous simulations showed that the coupling of gas turbines and SOFCs could reduce the impact of degradation as a result of lower fuel utilization and more flexible current demands. To improve the analytical capability of the model, degradation was incorporated on a distributed basis to identify localized effects and more accurately assess potential failure mechanisms. For syngas fueled systems, the results showed that current density shifted to underutilized sections of the fuel cell as degradation progressed. Over-all, the time to failure was increased, but the temperature difference along cell was increased to unacceptable levels, which could not be determined from the previous approach.

Optimizing Maintenance for Multi-Unit Industrial Gas Turbine Installations

2010 ◽  
Author(s):  
Markus Bohlin ◽  
Mathias Wa¨rja
Keyword(s):  
Natural Gas ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Gas Turbines ◽  
Economic Effects ◽  
Negative Effects ◽  
Component Level ◽  
Maintenance Schedule ◽  
Natural Gas Pipelines ◽  
Oil Platforms

High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples where gas turbines are used include the mechanical drive in natural gas pipelines and power generation on oil platforms, where it is common to use redundant gas turbines to mitigate the effects of service outage. In this paper, component-level maintenance of parallel multi-unit systems is considered, allowing production at a reduced level when some of the units are not operational. Units are themselves assumed to be composed out of components in a serial configuration; maintenance of one component implies shutdown of the unit. Parallel installations allow maintenance to be performed on one or a few gas turbines without taking down the entire installation. This allows maintenance to be optimized even further than in a serial system. However, the maintenance optimization process is made more complicated, since there now exist both positive and negative grouping effects. The positive grouping effects come from shared setup activities and costs, and the negative effects come from resource limitations, in this case the limited number of gas turbines which can be maintained at the same time. In the approach presented in this paper, each component has its individual preventive maintenance schedule, which is updated at inspections, changes in production and when indicated using remote condition monitoring. A minimal repair model for noncritical routine inspections and service tasks is assumed, which does not affect component state. In addition, previously developed procedures for estimating and measuring residual component lifetime for individual components during operation are used. The procedures are based on a Retirement For Cause (RFC) approach where components are not replaced until a potential failure has been detected. To maximize revenues for an operator, the available information is evaluated using software where scenario analysis and optimization is performed. To show the possible economic effects, gas turbine operation data is used together with maintenance and operator requirements as input for optimization of a production line consisting of a natural-gas compressor station having three SGT-600 gas turbines. Savings can be substantial compared to a traditional preventive maintenance plan.

An Automated Energy Conservation Decision Support System for Navy Gas Turbines

2010 ◽  
Author(s):  
Michael J. Roemer ◽  
Carl A. Palmer ◽  
Sudarshan P. Bharadwaj ◽  
Chris Savage
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Energy Conservation ◽  
Real Time ◽  
Fuel Consumption ◽  
Support System ◽  
Gas Turbines ◽  
Fuel Management ◽  
Real Time Information ◽  
Time Information

Energy conservation measures currently employed by U.S. Navy surface combatants require labor-intensive, time-consuming data entry from which fuel curves are generated to drive each ship’s propulsion plant machinery alignment. From these rudimentary curves optimal transit speeds, configurations, and refueling requirements are determined for specific operational demands and mission profiles. This paper describes an automated process for optimizing shipboard fuel consumption rates by integrating advanced diagnostic and maintenance optimization techniques with the onboard data information system. The automated energy conservation decision support system described herein addresses fossil fuel propulsion (gas turbines, steam turbines, and diesel engines), power generation and auxiliary systems. The software tool consists of diagnostic, fuel management, and maintenance modules. The diagnostic module tracks and trends the health state of components that use fuel (and their supporting systems) to provide real-time information on the impact of their current condition on fuel consumption. The fuel management module automates data collection and the generation of fuel curves through open-systems architecture communication with ICAS. It also enables planning by recommending an optimal machinery configuration to minimize fuel consumption based on either speed or time to destination constraints. Additionally, a fuel management module provides real-time information on fuel consumption and optimizes the load of each component based on its health condition, operating requirements and the number and condition of similar components. Finally, overall decision support comes from the maintenance management module that tracks the maintenance actions being performed on fuel consuming systems and recommends future maintenance to be performed (from a fuel conservation standpoint) based on current health information.

Development of Real Time High Energy X-Ray Imaging System for Use in Dynamic Fluoroscopy of Aero Gas Turbines

1975 ◽  
Author(s):  
A. E. Stewart
Keyword(s):  
Real Time ◽  
Gas Turbines ◽  
Imaging System ◽  
High Energy ◽  
Cesium Iodide ◽  
Imaging Systems ◽  
X Ray ◽  
X Ray Imaging ◽  
Different Types

This paper discusses the development of a real-time high energy x-ray imaging system for use in dynamic fluoroscopy of aero gas turbines. In order to cover the range of subjects on gas turbines, over ten combinations of film and screen types are used. Three different types of x-ray imaging systems were considered for use: direct type intensifiers (cesium iodide phosphors), and indirect type intensifiers — Marconi “Marionette” and the Oude Delft “Delcalix.”

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