Development of Small Hydroelectric Power Plant Maintenance Costs using Chaos Embedded Adaptive Particle Swarm Optimization

Year 2023, Volume: 9 Issue: 4, 788 - 803, 22.12.2023

Soner Çelikdemir , Mahmut Temel Özdemir

https://doi.org/10.28979/jarnas.1197546

Abstract

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.

Keywords

Small Hydroelectric Power Plants, Maintenance Cost Estimation, Sensitivity Analysis, Chaos embedded adaptive particle swarm optimization, Correlation model

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