JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Predictive Model of the Generator Output Based on the Learning of Performance Data in Power Plant
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Predictive Model of the Generator Output Based on the Learning of Performance Data in Power Plant
Yang, HacJin; Kim, Seong Kun;
  PDF(new window)
 Abstract
Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.
 Keywords
Generator Output;Performance Test;Support Vector Machines;Turbine Cycle;Verification;
 Language
Korean
 Cited by
 References
1.
S. K. Kim and K. H. Choi, "Development of Performance Analysis System (NOPAS) for Turbine Cycle of Nuclear Power Plant", Journal of Korean Nuclear Society, Vol. 33, No. 1, pp.211-218, 2001.

2.
S. K. Kim and K. H. Choi, "Development of Performance Analysis Methodology for Nuclear Power Plant Turbine Cycle using Validation Model of Performance Measurements", Journal of KSME, Vol. 24, No. 12, pp.1625-1634, 2001.

3.
S. K. Kim and K. H. Choi, "Thermal Performance Analysis System Based on Measurement Validation for Nuclear Power Plant", 4th KSME-JSME Thermal Engineering Conference, 2000.

4.
Korea Hydraulic and Nuclear Co., "User's Guide for PERUPS, Programmer's Guide for PERUPS, Technological Guide for PERUPS", Research Report, Korea Hydraulic and Nuclear Co., 2003.

5.
Korea Hydraulic and Nuclear Co., "Development of Thermal Performance Analysis Computer Program on Turbine Cylce of Yongwang 3, 4 Units", Research Report, Korea Hydraulic and Nuclear Co., 2003.

6.
C. J. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowlege Discovery, Vol. 2, pp.121-167, 1998. DOI: http://dx.doi.org/10.1023/A:1009715923555 crossref(new window)

7.
Nello Cristianini and John Shawe-Taylor, "Introduction to Support Vector Machines and other Kernel-based Learning Method", Cambridge University Press, 2000.

8.
S. K. Kim and K. H. Choi, "PERUPS (PERgromance UPgrade System) for On-line Performance Analysis of Turbine Cycle of Nuclear Power Plant", Journal of Korean Nuclear Society, Vol. 37, No.2, pp.165-172, 2005.

9.
H. J. Yang, S. K. Kim and K. H. Choi, "A Study of the Feature Classification and the Predictive Model of Main Feed-Water Flow for Turbine Cycle", Journal of Energy Engineering, Vol. 23, No. 4, pp.263-271, 2014. DOI: http://dx.doi.org/10.5855/ENERGY.2014.23.4.263 crossref(new window)