Abstract
Hydropower is claimed to be one of the least expensive but most reliable sources of renewable energy. The frequency of power generation depends directly on the flow of water on which the power production facility has been constructed. The flow of water depends on the upstream rainfall, which contributes to the surface runoff to create the flow in the channel which rotates the turbine for production of electricity. The utilization factor of a hydropower plant (HPP) is defined as the ratio between the energy actually produced to the energy production capacity of the hydropower plant (HPP). It is synonymous with load factor if the capacity of the HPP and the maximum energy produced become equal. The present study will aim to identify the optimal zones where minimum rainfall and maximum utilization can be achieved by employing particle swarm optimization within the known constraints of small scale hydropower plant. The result of the study will highlight the adjustments required to be followed in the hydropower plants in generating optimal power output even in the days of scarce rainfall.
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References
Chau KW (2006) Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J Hydrol 329(3–4):363–367
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, 4–6 October 1995, Nagoya, Japan, pp 39–43
Feng Y, Zheng B, Li Z (2010) Exploratory study of sorting particle swarm optimizer for multi-objective design optimization. Math Comput Model 52(11–12):1966–1975
Guo CX, Zhao B (2006) A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch. J Zhejiang Uni Sci A 7(4):615–622
HDR (1998) Consumption for human development. Retrieved from http://hdr.undp.org/en/reports/global/hdr1998/chapters/
Jia DL, Zheng GX, Qu BY, Khan MK (2011) A hybrid particle swarm optimization algorithm for high-dimensional problems. Comput Ind Eng 61(4):1117–1122
Khajehzadeh M, Taha MR, El-Shafie A (2011) Reliability analysis of earth slopes using hybrid chaotic particle swarm optimization. J Cent South Univ Technol 18(5):1626–1637
López LFM, Blas NG, Arteta A (2012) The optimal combination: grammatical swarm, particle swarm optimization and neural networks. J Comput Sci 3(1–2):46–55
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8(3):204–210
Mingo LFL, Blas NG, Arteta A (2012) The optimal combination: grammatical swarm, particle swarm optimization and neural networks. J Comput Sci 3(1–2):46–55
Mousa AA, El-Shorbagy MA, Abd-El-Wahed WF (2012) Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm Evolut Comput 3:1–14
Mukhopadhyay S, Banerjee S (2012) Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization. Expert Syst Appl 39(1):917–924
Nakicenovic (2012) World Energy Assessment Report. Retrieved from http://webarchive.iiasa.ac.at/Research/TNT/WEB/Publications/The_World_Energy_Assessment_Re/the_world_energy_assessment_re.html
Parsopoulos KE, Vrahatis MN (2007) Parameter selection and adaptation in Unified Particle Swarm Optimization. Math Comput Model 46(1–2):198–213
REN21 (2011a) Renewables 2011: global status report. Retrieved from http://www.ren21.net/Portals/97/documents/GSR/GSR2011_Master18.pdf
REN21 (2011b) Renewables 2011: global status report, p 18. Retrieved from http://www.ren21.net/Portals/97/documents/GSR/GSR2011_Master18.pdf
Sahoo NC, Ganguly S, Das D (2012) Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization. Swarm Evolut Comput 3:15–32
Shelokar PS, Siarry P, Jayaraman VK, Kulkarni BD (2007) Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl Math Comput 188(1):129–142
Tsai CY, Kao IW (2011) Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 38(6):6565–6576
Wang J, Zhu S, Zhao W, Zhu W (2011) Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm. Expert Syst Appl 38(7):8151–8158
Xinchao Z (2010) A perturbed particle swarm algorithm for numerical optimization. Appl Soft Comput 10(1):119–124
Zhang Y, Gong DW, Ding ZH (2011) Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer. Expert Syst Appl 38(11):13933–13941
Zhang Y, Gong DW, Ding Z (2012) A Bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inform Sci 192:213–227
Zhao Y, Zu W, Zeng H (2009) A modified particle swarm optimization via particle visual modeling analysis. Comput Math Appl 57(11–12):2022–2029
Zhao L, Qian F, Yang Y, Zeng Y, Su H (2010) Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization. Appl Soft Comput 10(3):938–944
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Majumder, M., Ghosh, S., Barman, R.N. (2013). Tradeoff Analysis Between Rainfall and Load Factor of a Small-Scale Hydropower Plant by Particle Swarm Optimization. In: Majumder, M., Barman, R. (eds) Application of Nature Based Algorithm in Natural Resource Management. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5152-1_3
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DOI: https://doi.org/10.1007/978-94-007-5152-1_3
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