A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant
This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties...
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Institute of Physics Publishing
2023
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| author | Aziz N.L.A.A. Yap K.S. Bunyamin M.A. |
| author2 | 55812399400 |
| author_facet | 55812399400 Aziz N.L.A.A. Yap K.S. Bunyamin M.A. |
| author_sort | Aziz N.L.A.A. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of �computing the word�. The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions. � Published under licence by IOP Publishing Ltd. |
| format | Conference paper |
| id | my.uniten.dspace-30237 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Physics Publishing |
| record_format | dspace |
| spelling | my.uniten.dspace-302372023-12-29T15:45:46Z A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant Aziz N.L.A.A. Yap K.S. Bunyamin M.A. 55812399400 24448864400 55812855600 Fault detection Fuzzy logic Learning systems Waterworks Circulating water system Extreme learning machine Fuzzy logic system Improving efficiency Mathematical tools Natural languages Overall efficiency Power generation plants algorithm electricity generation energy efficiency fuzzy mathematics natural resource numerical model pollution monitoring uncertainty analysis Knowledge acquisition This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of �computing the word�. The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions. � Published under licence by IOP Publishing Ltd. Final 2023-12-29T07:45:46Z 2023-12-29T07:45:46Z 2013 Conference paper 10.1088/1755-1315/16/1/012102 2-s2.0-84881106611 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881106611&doi=10.1088%2f1755-1315%2f16%2f1%2f012102&partnerID=40&md5=3aad5ca69d775ca08be5246ccc54376b https://irepository.uniten.edu.my/handle/123456789/30237 16 1 12102 All Open Access; Gold Open Access Institute of Physics Publishing Scopus |
| spellingShingle | Fault detection Fuzzy logic Learning systems Waterworks Circulating water system Extreme learning machine Fuzzy logic system Improving efficiency Mathematical tools Natural languages Overall efficiency Power generation plants algorithm electricity generation energy efficiency fuzzy mathematics natural resource numerical model pollution monitoring uncertainty analysis Knowledge acquisition Aziz N.L.A.A. Yap K.S. Bunyamin M.A. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title | A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title_full | A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title_fullStr | A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title_full_unstemmed | A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title_short | A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| title_sort | hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant |
| topic | Fault detection Fuzzy logic Learning systems Waterworks Circulating water system Extreme learning machine Fuzzy logic system Improving efficiency Mathematical tools Natural languages Overall efficiency Power generation plants algorithm electricity generation energy efficiency fuzzy mathematics natural resource numerical model pollution monitoring uncertainty analysis Knowledge acquisition |
| url_provider | http://dspace.uniten.edu.my/ |