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Abstract
The large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation. Operating data from the years 2015–2017 were used to calculate the availability by traditional method (Weibull distribution) and train the ANNs, while data from the year 2018 of operation were used to verify the model. The study implies that the ANN may be able to forecast the availability of the generator with a correlation coefficient (R) 0.99874 and a Mean Square Error (MSE) 5.6937E-06 between the availability predicted by ANN and Weibull distribution output.
References
[2] Walter, Eric, and Luc Pronzato, Identification of Parametric Models from Experimental Data. Communications and Control Engineering, Springer, 1997.
[3] Zouhair Issa Ahmed, Bahaa I. Kazem, Ahmed A. R. Al-khafaji, “Availability Estimation of Caustic Pump by Using Artificial Neural Network,” The 6th Engineering Conference: College of Engineering, vol. 3: Mechanical and Nuclear Engineering, 2009.
[4] Hanumant P. Jagtap , Anand K. Bewoor , Ravinder Kumar, Mohammad Hossein Ahmadi, Mamdouh El Haj Assad, and Mohsen Sharifpur, “RAM analysis and availability optimization of thermal power plant water circulation system using PSO,” Energy Reports, vol. 7, pp. 1133-1153, 2021. https://doi.org/10.1016/j.egyr.2020.12.025
[5] Ling Wang, Haijun Hu, Yuqiao Wang, Wei Wu, and Pengfei He, “The availability model and parameters estimation method for the delay time model with imperfect maintenance at inspection,” Applied Mathematical Modelling, vol. 35, pp. 2855-2863, 2011. doi:10.1016/j.apm.2010.11.070
[6] Rajpal, P. S., K. S. Shishodia, and G. S. Sekhon, “An artificial neural network for modeling reliability, availability and maintainability of a repairable system,” Reliability Engineering & System Safety, vol. 91, no. 7, pp. 809-819, 2006. doi:10.1016/j.ress.2005.08.004
[7] M. K. Bhuyan, D. P. Mohapatra, and S. Sethi, “Software Reliability Assessment using Neural Networks of Computational Intelligence Based on Software Failure Data,” Baltic Journal of Modern Computing, vol. 4, no. 4, pp. 1016-1037, 2016. http://dx.doi.org/10.22364/bjmc.2016.4.4.26
[8] Kutyłowska Małgorzata, “Neural network approach for availability indicator prediction,” Periodica Polytechnica Civil Engineering, vol. 61, no. 4 , pp. 873-881 ,2017. https://doi.org/10.3311/PPci.10429
[9] Savsar Mehmet, “Availability analysis of a power plant by computer simulation,” International Journal of Energy and Power Engineering., vol. 9, no.4, pp. 495-498, 2015.
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