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Research Papers: Gas Turbines: Turbomachinery

Stable and Efficient Operation of Gas Compressor With Improving of Surge Detection System

[+] Author and Article Information
Sayyid Mahdi Alavinia

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: zalloi@shahroodut.ac.ir

Mohammad Ali Sadrnia

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: masadrnia@shahroodut.ac.ir

Mohammad Javad Khosrowjerdi

Department of
Electrical Engineering,
Sahand University of Technology,
Tabriz 51335-1996, Iran
e-mail: khosrowjerdi@sut.ac.ir

Mohammad Mehdi Fateh

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: mmfateh@shahroodut.ac.ir

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 29, 2014; final manuscript received March 22, 2014; published online May 9, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(10), 102602 (May 09, 2014) (10 pages) Paper No: GTP-14-1072; doi: 10.1115/1.4027371 History: Received January 29, 2014; Revised March 22, 2014

This paper investigates the application of fault diagnosis (FD) approach for improving performance of compressors within exact operating point determination. Detecting of sensor fault or failure status is more important in the compressor for safety-critical application. No work has previously been reported on the use of the FD system within a compressor surge-suppressing system. Therefore, the main contribution of this paper is presenting different and complementary techniques for surge-suppressing studies via sensor FD. By data acquisition from a nonlinear Moore–Greitzer model, a neural network (NN) and innovation complex decision logic provide residual generation and evaluation blocks in an analytical redundancy FD system, respectively. The proposed FD deals with the most-common sensor faults and failures in seven different scenarios according to their nature, such as bias, cutoff, loss of efficiency, and freeze.

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References

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Figures

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Fig. 1

A schematic view of TC

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Fig. 2

FD based on analytical redundancy

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Fig. 3

A schematic of compressor control system

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Fig. 4

(a) Filtered Φ, (b) filtered Ψ, (c) filtered J, and (d) filtered B

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Fig. 5

Compressor performance curve

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Fig. 6

B variable for multiple inputs

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Fig. 7

Mean squares error performance function

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Fig. 8

Proposed residual generator

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Fig. 9

Fault free scenario

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Fig. 11

B sensor cutoff and unstable system

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Fig. 12

(a) Φ freeze, (b) Ψ cutoff, and (c) B degradation

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Fig. 13

FD in compressor operating point determining

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