Modeling and Predicting Steam Power Plant Condenser Vacuum based on Small-sized Operation Data

Authors

  • Aqli Mursadin Mechanical Engineering Program, Lambung Mangkurat University, Indonesia
  • Andi Yuwenda Iriyanto UPK Asam-asam, PT PLN Indonesia Power, Indonesia
Volume: 14 | Issue: 4 | Pages: 15233-15238 | August 2024 | https://doi.org/10.48084/etasr.7574

Abstract

The condenser vacuum is an important variable in steam power plants. Monitoring and controlling this variable requires predicting its behavior. This paper develops further Autoregressive-Generalized Autoregressive Conditional Heteroscedasticity (AR-GARCH) models for this purpose, using lagged values of predictors. The predictors include the inlet temperature of the condenser cooling water and the active power of the generator. Models can be adequately trained with small-sized data, making them suitable for use in thermal plants, which are often regularly maintained with operating conditions being reset, rendering past data obsolete. Training and testing were carried out using operation data from an actual steam power plant generating unit during a period in which it faced the prospect of an emergency turbine shutdown. When the models pass all the required statistical tests, they tend to outperform other techniques, including autoregressive neural networks and support vector regression, in terms of prediction. This study also discusses an implementation scenario. The choice of training sizes and model variants can be flexible, enhancing the models' practicality for real operational situations. This study also provides additional directions for further research.

Keywords:

condenser vacuum, energy infrastructure asset, off-design condition, operation and maintenance, small-sized data, statistical learning, steam power plant, turbine trip

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References

S. Basu and A. K. Debnath, Power Plant Instrumentation and Control Handbook: A Guide to Thermal Power Plants. London, UK: Academic Press, 2014.

Z. R. Labidi, H. Schulte, and A. Mami, "A Systematic Controller Design for a Photovoltaic Generator with Boost Converter Using Integral State Feedback Control," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 4030–4036, Apr. 2019.

D. V. Doan, K. Nguyen, and Q. V. Thai, "Load-Frequency Control of Three-Area Interconnected Power Systems with Renewable Energy Sources Using Novel PSO~PID-Like Fuzzy Logic Controllers," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8597–8604, Jun. 2022.

M. Masmali, M. I. Elimy, M. Fterich, E. Touti, and G. Abbas, "Comparative Studies on Load Frequency Control with Different Governors connected to Mini Hydro Power Plant via PSCAD Software," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12975–12983, Feb. 2024.

D. K. Sarkar, Ed., "Thermal Power Plant - Pre-Operational Activities," in Thermal Power Plant, Amsterdam, Netherlands: Elsevier, 2017.

H. Kumar, Rahul, S. Verma, and S. Bera, "Analysis of Machine Learning algorithms for prediction of Condenser Vacuum in Thermal Power Plant," in 2020 International Conference on Electrical and Electronics Engineering (ICE3), Gorakhpur, India, Feb. 2020, pp. 778–783.

Z. Sun et al., "A Novel Condenser Vacuum Degree Prediction Model Based on LSTM and MemN2N," Journal of Physics: Conference Series, vol. 2294, no. 1, Mar. 2022, Art. no. 012030.

P. He et al., "Condenser Vacuum Degree Prediction Model with Multi-View Information Fusion," Journal of Physics: Conference Series, vol. 2294, no. 1, Mar. 2022, Art. no. 012032.

A. Mursadin and A. Y. Iriyanto, "Modeling Steam Power Plant Condenser Vacuum under Off-design Conditions: A Statistical Approach," in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, Nov. 2023, pp. 266–271.

R. F. Engle, "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, vol. 50, no. 4, pp. 987–1007, 1982.

T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, vol. 31, no. 3, pp. 307–327, Apr. 1986.

C. Francq and J. M. Zakoian, GARCH Models: Structure, Statistical Inference and Financial Applications. Hoboken, NJ, USA: John Wiley & Sons, 2019.

L. Pan, D. Flynn, and M. Cregan, "Statistical Model for Power Plant Performance Monitoring and Analysis," in 2007 42nd International Universities Power Engineering Conference, Brighton, UK, Sep. 2007, pp. 121–126.

K. Świrski, "Power Plant Performance Monitoring Using Statistical Methodology Approach," Journal of Power Technologies, vol. 91, no. 2, pp. 63–76, 2011.

S. Chandrasekharan, R. C. Panda, and B. N. Swaminathan, "Statistical modeling of an integrated boiler for coal fired thermal power plant," Heliyon, vol. 3, no. 6, Jun. 2017, Art. no. e00322.

G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015.

D. B. Nelson, "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, vol. 59, no. 2, pp. 347–370, 1991.

C. Fernández and M. F. J. Steel, "On Bayesian Modeling of Fat Tails and Skewness," Journal of the American Statistical Association, vol. 93, no. 441, pp. 359–371, Mar. 1998.

G. M. Ljung and G. E. P. Box, "On a measure of lack of fit in time series models," Biometrika, vol. 65, no. 2, pp. 297–303, Aug. 1978.

R. F. Engle and V. K. Ng, "Measuring and Testing the Impact of News on Volatility," The Journal of Finance, vol. 48, no. 5, pp. 1749–1778, 1993.

H. Pham, "Basic Statistical Concepts," in Springer Handbooks, Springer, 2006, pp. 3–48.

J. Nyblom, "Testing for the Constancy of Parameters over Time," Journal of the American Statistical Association, vol. 84, no. 405, pp. 223–230, Mar. 1989.

J. de Leeuw, "Introduction to Akaike (1973) Information Theory and an Extension of the Maximum Likelihood Principle," in Breakthroughs in Statistics: Foundations and Basic Theory, S. Kotz and N. L. Johnson, Eds. New York, NY: Springer, 1992, pp. 599–609.

"R: The R Project for Statistical Computing." https://www.r-project.org/.

A. Galanos and T. Kley, "rugarch: Univariate GARCH Models." Sep. 20, 2023, [Online]. Available: https://cran.r-project.org/web/packages/rugarch/.

A. Botchkarev, "A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms," Interdisciplinary Journal of Information, Knowledge, and Management, vol. 14, pp. 045–076, Jan. 2019.

R. Hyndman et al., "Forecasting Functions for Time Series and Linear Models." [Online]. Available: https://pkg.robjhyndman.com/forecast/.

D. Meyer et al., "e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien." [Online]. Available: https://cran.r-project.org/web/packages/e1071/.

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How to Cite

[1]
Mursadin, A. and Iriyanto, A.Y. 2024. Modeling and Predicting Steam Power Plant Condenser Vacuum based on Small-sized Operation Data. Engineering, Technology & Applied Science Research. 14, 4 (Aug. 2024), 15233–15238. DOI:https://doi.org/10.48084/etasr.7574.

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