Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization
In this study, a new equation model is proposed to improve the maintenance costs of Small Scale Hydroelectric Power Plants (SHPP). The proposed equation model consists of 4 terms and 7 parameters using the Chaos Embedded Adaptive Particle Swarm Optimization (CEAPSO). The MATLAB program was used to c...
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Çanakkale Onsekiz Mart University
2023-12-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/2743067 |
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| author | Mahmut Temel Özdemir Soner Çelikdemir |
| author_facet | Mahmut Temel Özdemir Soner Çelikdemir |
| author_sort | Mahmut Temel Özdemir |
| collection | DOAJ |
| description | In this study, a new equation model is proposed to improve the maintenance costs of Small Scale Hydroelectric Power Plants (SHPP). The proposed equation model consists of 4 terms and 7 parameters using the Chaos Embedded Adaptive Particle Swarm Optimization (CEAPSO). The MATLAB program was used to calculate the parameters in the proposed equation model. In this study, the main error value for 14 maintenance items required for a SHPP is calculated as 17.4819%. The maintenance cost of a SHPP to be installed in this way can be predicted with high accuracy using the proposed equation model. In the study, the sensitivity analysis of the proposed equation model is also performed, and maintenance cost changes are expressed in different parameter values. In the study, corrected data from 8 SHPP in India are used. These data cover the maintenance costs of all components for the years 2015-2016. In the study, unlike the literature, the flow parameter is added to the power and head parameters. In this way, a more sensitive equation model is developed for SHPP data. In addition, realistic results are obtained by applying constraints to the parameters. Considering the 14 different maintenance cost parameters examined in the study, a correlation model is proposed to give better results than the literature for other maintenance costs except the power channel and penstock cost. |
| format | Article |
| id | doaj-art-dabead8ba35345acad0ef5cf4ed85f13 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-dabead8ba35345acad0ef5cf4ed85f132025-02-05T17:57:35ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952023-12-019478880310.28979/jarnas.1197546453Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm OptimizationMahmut Temel Özdemir0https://orcid.org/0000-0002-5795-2550Soner Çelikdemir1https://orcid.org/0000-0002-1419-3398FIRAT UNIVERSITY, FACULTY OF ENGINEERINGBİTLİS EREN ÜNİVERSİTESİ, TATVAN MESLEK YÜKSEKOKULUIn this study, a new equation model is proposed to improve the maintenance costs of Small Scale Hydroelectric Power Plants (SHPP). The proposed equation model consists of 4 terms and 7 parameters using the Chaos Embedded Adaptive Particle Swarm Optimization (CEAPSO). The MATLAB program was used to calculate the parameters in the proposed equation model. In this study, the main error value for 14 maintenance items required for a SHPP is calculated as 17.4819%. The maintenance cost of a SHPP to be installed in this way can be predicted with high accuracy using the proposed equation model. In the study, the sensitivity analysis of the proposed equation model is also performed, and maintenance cost changes are expressed in different parameter values. In the study, corrected data from 8 SHPP in India are used. These data cover the maintenance costs of all components for the years 2015-2016. In the study, unlike the literature, the flow parameter is added to the power and head parameters. In this way, a more sensitive equation model is developed for SHPP data. In addition, realistic results are obtained by applying constraints to the parameters. Considering the 14 different maintenance cost parameters examined in the study, a correlation model is proposed to give better results than the literature for other maintenance costs except the power channel and penstock cost.https://dergipark.org.tr/en/download/article-file/2743067small hydroelectric power plantsmaintenance cost estimationsensitivity analysischaos embedded adaptive particle swarm optimizationcorrelation model |
| spellingShingle | Mahmut Temel Özdemir Soner Çelikdemir Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization Journal of Advanced Research in Natural and Applied Sciences small hydroelectric power plants maintenance cost estimation sensitivity analysis chaos embedded adaptive particle swarm optimization correlation model |
| title | Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization |
| title_full | Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization |
| title_fullStr | Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization |
| title_full_unstemmed | Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization |
| title_short | Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization |
| title_sort | development of small hydroelectric power plant maintenance costs using chaos embedded adaptive particle swarm optimization |
| topic | small hydroelectric power plants maintenance cost estimation sensitivity analysis chaos embedded adaptive particle swarm optimization correlation model |
| url | https://dergipark.org.tr/en/download/article-file/2743067 |
| work_keys_str_mv | AT mahmuttemelozdemir developmentofsmallhydroelectricpowerplantmaintenancecostsusingchaosembeddedadaptiveparticleswarmoptimization AT sonercelikdemir developmentofsmallhydroelectricpowerplantmaintenancecostsusingchaosembeddedadaptiveparticleswarmoptimization |