A Review of Gas Turbine Flow Path Analysis: From Paper Calculation to Artificial Neural Networks

2002 ◽  
Author(s):  
Magnus Genrup ◽  
Mohsen Assadi ◽  
Tord Torisson
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Analysis ◽  
Gas Turbine ◽  
Flow Path ◽  
Third Party ◽  
Spreadsheet Program ◽  
Artificial Neural ◽  
Analysis System ◽  
Turbine Flow Path

Today many methods are available for gas turbine flow path analysis. Some of them are very simple but yet very useful, since they give an indication of the compressor capacity with almost no calculation effort. The state of the art today is the heat and mass balance models (HMB), which are more sophisticated. This paper presents a general overview of these methods, including the most recent trend, Artificial Neural Networks (ANN). In the future, the ANN-based flow path analysis system will probably, to some extent, replace the HMB-based systems, or become a complementary tool for monitoring and performance analysis of power production units. This paper will give a comprehensive explanation of how to build a flow path analysis system in an equation-solving package (e.g. spreadsheet program), by using relationships presented here. This may give a system that is well within the capabilities of most commercially available systems, used and developed by consultant companies (third party companies).

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2014 ◽  
Author(s):  
Dengji Zhou ◽  
Jiayun Wang ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Artificial Neural ◽  
Fault Diagnostic ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like non-linear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like artificial neural networks, need a large number of experimental data. Both are difficult to gain. Support vector machine (SVM), a novel computational learning method with excellent performance, seems to be a good choice for gas path fault diagnostic of gas turbine without engine model. In this paper, SVM is employed to diagnose a deteriorated gas turbine. And the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data, and can be employed to gas path fault diagnostic of gas turbine. Additionally, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnostic based on small sample.

Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks

2003 ◽  
Author(s):  
Pernilla Olausson ◽  
Daniel Ha¨ggsta˚hl ◽  
Jaime Arriagada ◽  
Erik Dahlquist ◽  
Mohsen Assadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Hybrid Model ◽  
Power Plants ◽  
Computational Time ◽  
Process Identification ◽  
Calculation Time ◽  
Artificial Neural

Traditionally, when process identification, monitoring and diagnostics are carried out for power plants and engines, physical modeling such as heat and mass balances, gas path analysis, etc. is utilized to keep track of the process. This type of modeling both requires and provides considerable knowledge of the process. However, if high accuracy of the model is required, this is achieved at the expense of computational time. By introducing statistical methods such as Artificial Neural Networks (ANNs), the accuracy of the complex model can be maintained while the calculation time is often reduced significantly reduced. The ANN method has proven to be a fast and reliable tool for process identification, but the step from the traditional physical model to a pure ANN model is perhaps too wide and, in some cases, perhaps unnecessary also. In this work, the Evaporative Gas Turbine (EvGT) plant was modeled using both physical relationships and ANNs, to end up with a hybrid model. The type of architecture used for the ANNs was the feed-forward, multi-layer neural network. The main objective of this study was to evaluate the viability, the benefits and the drawbacks of this hybrid model compared to the traditional approach. The results of the case study have clearly shown that the hybrid model is preferable. Both the traditional and the hybrid models have been verified using measured data from an existing pilot plant. The case study also shows the simplicity of integrating an ANN into conventional heat and mass balance software, already implemented in many control systems for power plants. The access to a reliable and faster hybrid model will ultimately give more reliable operation, and ultimately the lifetime profitability of the plant will be increased. It is also worth mentioning that for diagnostic purposes, where advanced modeling is important, the hybrid model with calculation time well below one second could be used to advantage in model predictive control (MPC).

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