An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan

A power generating system in a steam power plant is a complex one. This system involves a large number of variables containing information concerning the operation conditions. It can also be seen as an important asset in both power generation and energy portfolios. At the Asam-asam power plant of So...

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Main Author: Mursadin Aqli
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20184301003
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spelling doaj-2322c63c50ca4afdb25340050e1917682021-02-02T03:40:12ZengEDP SciencesE3S Web of Conferences2267-12422018-01-01430100310.1051/e3sconf/20184301003e3sconf_astechnova2017_01003An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South KalimantanMursadin AqliA power generating system in a steam power plant is a complex one. This system involves a large number of variables containing information concerning the operation conditions. It can also be seen as an important asset in both power generation and energy portfolios. At the Asam-asam power plant of South Kalimantan, the complexity of the system has led to difficulties in explaining the apparently steady decrease in the output power. The amount of data collected is simply too big and the dimension too high for analysis purposes based on conventional thermodynamics. This study was performed to tackle the problem using statistical modelling. This approach can accommodate empirical behaviors of the variables and the probabilistic nature of the system. Information obtained from this modelling can be valuable for various purposes. The method consists of literature review, model development, data collection and analysis, and model fitting. Generalized additive models were chosen. Data were available from the company as observed from more than 140 variables. The resulting model identifies variables significantly related to the output power and locate subsystems whose fluctuating behaviors are usually ignored in a conventional thermodynamic analysis. A direction for future research is recommended.https://doi.org/10.1051/e3sconf/20184301003
collection DOAJ
language English
format Article
sources DOAJ
author Mursadin Aqli
spellingShingle Mursadin Aqli
An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
E3S Web of Conferences
author_facet Mursadin Aqli
author_sort Mursadin Aqli
title An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
title_short An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
title_full An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
title_fullStr An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
title_full_unstemmed An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan
title_sort application of statistical modelling on power generating asset performance analysis at the asam-asam steam power plant of south kalimantan
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2018-01-01
description A power generating system in a steam power plant is a complex one. This system involves a large number of variables containing information concerning the operation conditions. It can also be seen as an important asset in both power generation and energy portfolios. At the Asam-asam power plant of South Kalimantan, the complexity of the system has led to difficulties in explaining the apparently steady decrease in the output power. The amount of data collected is simply too big and the dimension too high for analysis purposes based on conventional thermodynamics. This study was performed to tackle the problem using statistical modelling. This approach can accommodate empirical behaviors of the variables and the probabilistic nature of the system. Information obtained from this modelling can be valuable for various purposes. The method consists of literature review, model development, data collection and analysis, and model fitting. Generalized additive models were chosen. Data were available from the company as observed from more than 140 variables. The resulting model identifies variables significantly related to the output power and locate subsystems whose fluctuating behaviors are usually ignored in a conventional thermodynamic analysis. A direction for future research is recommended.
url https://doi.org/10.1051/e3sconf/20184301003
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