Power Optimization of Electric Developments in Diesel Power Plant for the Electrical Energy Sources using Dynamic Programming Algorithm
Abstract
Malang was more than 85 kVA. All electrical devices could be
activated; but when the energy source was inactive, all electricity
requirements were transferred to the diesel power plant (DPP).
However, the electrical capacity of DPP was only 20 kVA;
therefore, it was necessary to optimize the electrical power load so
that the DPP energy could be absorbed optimally using the room
scheduling and electrical devices priority systems. The Dynamic
Programming Algorithm was embedded in the power optimization
system to help optimize the work. The power optimization prototype
was used to simulate the 1st floor of the G4 Building’s condition.
The system consisted of a controller, a central controller, and a
user interface. the controller comprised of a current sensor,
microcontroller, and a relay. The central controller consisted of
Raspberry Pi 3 hardware that was installed as the server to answer
the HTTP request from the controller and user interface. The user
interface was displayed in a dynamic web to ease the user in
managing the electrical devices and entering the room usage
schedule. The power optimization system managed the electrical
energy from DPP by turning on the electrical devices according to
the priority value. The power optimization system tests were divided
into six problems, of which each stage had an error value of 0%.
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F. S. Abu-Mouti and M. E. El-Hawary, “Optimal Distributed Generation Allocation and Sizing in
Distribution Systems via Artificial Bee Colony Algorithm,” IEEE Trans. Power Deliv., vol. 26, no. 4,
pp. 2090–2101, Oct. 2011, doi: 10.1109/TPWRD.2011.2158246.
Vol. 1, No. 1, January 2019, pp. 20-26
K. Chandram, N. Subrahmanyam, and M. Sydulu, “Equal embedded algorithm for economic load
dispatch problem with transmission losses,” Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 500–
, Mar. 2011, doi: 10.1016/j.ijepes.2010.12.002.
İ. Erozan, “A fuzzy decision support system for managing maintenance activities of critical components
in manufacturing systems,” J. Manuf. Syst., vol. 52, pp. 110–120, Jul. 2019, doi:
1016/j.jmsy.2019.06.002.
E. Shayesteh, J. Yu, and P. Hilber, “Maintenance optimization of power systems with renewable energy
sources integrated,” Energy, vol. 149, pp. 577–586, Apr. 2018, doi: 10.1016/j.energy.2018.02.066.
M. C. Carnero and A. Gómez, “Maintenance strategy selection in electric power distribution systems,”
Energy, vol. 129, pp. 255–272, Jun. 2017, doi: 10.1016/j.energy.2017.04.100.
H. Mo, G. Sansavini, and M. Xie, “Performance-based maintenance of gas turbines for reliable control
of degraded power systems,” Mech. Syst. Signal Process., vol. 103, pp. 398–412, Mar. 2018, doi:
1016/j.ymssp.2017.10.021.
A. Medjber, A. Guessoum, H. Belmili, and A. Mellit, “New neural network and fuzzy logic controllers
to monitor maximum power for wind energy conversion system,” Energy, vol. 106, pp. 137–146, Jul.
, doi: 10.1016/j.energy.2016.03.026.
E. F. Ferreira and J. D. Barros, “Faults Monitoring System in the Electric Power Grid of Medium
Voltage,” Procedia Comput. Sci., vol. 130, pp. 696–703, 2018, doi: 10.1016/j.procs.2018.04.123.
T. S. Prasanna and P. Somasundaram, “Fuzzy mutated evolutionary programming based algorithm for
combined economic and emission dispatch,” in TENCON 2008 - 2008 IEEE Region 10 Conference,
Hyderabad, India, 2008, pp. 1–5, doi: 10.1109/TENCON.2008.4766769.
P. M. García-Vite, B. L. Reyes-García, C. L. Valdez-Hernández, and A. L. Martínez-Salazar,
“Microcontroller-based emulation of a PEM fuel cell,” Int. J. Hydrog. Energy, p. S0360319919337735,
Nov. 2019, doi: 10.1016/j.ijhydene.2019.10.034.
M. M. Al-Kofahi, M. Y. Al-Shorman, and O. M. Al-Kofahi, “Toward energy efficient microcontrollers
and Internet-of-Things systems,” Comput. Electr. Eng., vol. 79, p. 106457, Oct. 2019, doi:
1016/j.compeleceng.2019.106457.
J. R. Raj, S. M. K. Rahman, and S. Anand, “Microcontroller USB interfacing with MATLAB GUI for
low cost medical ultrasound scanners,” Eng. Sci. Technol. Int. J., vol. 19, no. 2, pp. 964–969, Jun. 2016,
doi: 10.1016/j.jestch.2016.01.008.
DOI: http://dx.doi.org/10.17977/um049v1i1p20-26
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