Issue

Vol. 8, No. 4, November 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Improving Fuel Consumption Efficiency of Synchronous Diesel Generator Operated at Adjustable Speed using Adaptive Inertia Weight Particle Swarm Optimization Algorithm

https://doi.org/10.22219/kinetik.v8i4`.1756
M Zaky Zaim Muhtadi
Institut Teknologi Sepuluh Nopember
Heri Suryoatmojo
Institut Teknologi Sepuluh Nopember
Soedibyo
Institut Teknologi Sepuluh Nopember
Mochamad Ashari
Institut Teknologi Sepuluh Nopember

Corresponding Author(s) : Mochamad Ashari

ashari@ee.its.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 4, November 2023

Abstract

Diesel generator is a reliable source of electricity, but it requires quite high operational costs, especially for fuel. This paper describes a technique for reducing fuel consumption in Diesel Engine Synchronous Generator systems. The system is originally a Constant Speed Diesel Synchronous Generator (CSD-SG), but during certain conditions, the speed is reduced to minimize fuel consumption by adjusting the Specific Fuel Consumption (SFC) map. SFC is defined as the amount of fuel consumed by a diesel engine generator for each unit of power output. It shows various numbers depending on the speed and operating power. In this paper, we use the Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) algorithm to select of the proper SFC curve at a certain speed and operating power. AIWPSO employs an adaptive inertial weight adjustment method, which enables this algorithm to achieve faster convergence than conventional Particle Swarm Optimisation (PSO) algorithms. The system is embedded with AC/DC/AC power electronics converter to regulate the frequency. Data set of 1000 kVA Cummins diesel engine generator from the oil and gas company in Central Java, Indonesia was taken for simulations. The results show that the AIWPSO algorithm calculates the fuel consumption as 1,678 liters per day on a typical condition, whereas in the previous method, the linear line needs 1,693 liters per day. Therefore, using AIWPSO method can save up to 450 liters of fuel per month. The simulation results show that the proposed method can improve fuel efficiency compared to the previous model.

Keywords

Adjustable speed diesel generator AC/DC/AC power electronic converters fuel reduction Adaptive Inertia Weight Particle Swarm Optimization
Muhtadi, M. Z. Z., Suryoatmojo, H., Soedibyo, & Ashari, M. (2023). Improving Fuel Consumption Efficiency of Synchronous Diesel Generator Operated at Adjustable Speed using Adaptive Inertia Weight Particle Swarm Optimization Algorithm . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(4`). https://doi.org/10.22219/kinetik.v8i4`.1756
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Mobarra M, Rezkallah M, Ilinca A. Variable Speed Diesel Generators: Performance and Characteristic Comparison. Energies. 2022 Jan 14;15(2):592.
  2. Soetrisno YAA, Winardi B, Sinuraya EW. Generator protection system with reverse power relay on 1000 KVA “Cummins” diesel generator at Cepu Human Resources Development Center. World J Adv Res Rev. 2022 Apr 30;14(1):377–84. https://doi.org/10.30574/wjarr.2022.14.1.0343
  3. Benhamed S, Ibrahim H, Belmokhtar K, Hosni H, Ilinca A, Rousse D, et al. Dynamic modeling of diesel generator based on electrical and mechanical aspects. In: IEEE Electrical Power and Energy Conference (EPEC) [Internet]. Ottawa, ON, Canada: IEEE; 2016 [cited 2019 Jan 27]. p. 1–6. Available from: http://ieeexplore.ieee.org/document/7771756/
  4. Sondor D, Tarasova ES. Variable Speed Diesel Generator (VSDG). In: Collection of scientific papers of the XIV All-Russian Scientific and Practical Conference. Tomsk: Изд-во ТПУ; 2014. p. 140–2.
  5. Hamilton J, Negnevitsky M, Wang X. Low load diesel perceptions and practices within remote area power systems. In: International Symposium on Smart Electric Distribution Systems and Technologies (EDST) [Internet]. Vienna, Austria: IEEE; 2015 [cited 2021 Jun 24]. p. 121–6. http://ieeexplore.ieee.org/document/7315194/
  6. Galkin SG, Tarnapowicz D, Matuszak Z, Jaskiewicz M. Optimization to Limit the Effects of Underloaded Generator Sets in Stand-Alone Hybrid Ship Grids. Energies. 2020 Feb 6;13(3):708. Optimization to Limit the Effects of Underloaded Generator Sets in Stand-Alone Hybrid Ship Grids
  7. Paper W. Adverse Effects of Low Load Operation on Diesel Generating Sets [Internet]. Asco Power Technologies; 2018 [cited 2022 Jul 5].
  8. Soto D. Modeling and measurement of specific fuel consumption in diesel microgrids in Papua, Indonesia. Energy for Sustainable Development. 2018 Aug;45:180–5. https://doi.org/10.1016/j.esd.2018.06.013
  9. Joshi MC, Gosala DB, Allen CM, Vos K, Van Voorhis M, Taylor A, et al. Reducing Diesel Engine Drive Cycle Fuel Consumption through Use of Cylinder Deactivation to Maintain Aftertreatment Component Temperature during Idle and Low Load Operating Conditions. Front Mech Eng. 2017 Aug 8;3:8. https://doi.org/10.3389/fmech.2017.00008
  10. Allen CM, Joshi MC, Gosala DB, Shaver GM, Farrell L, McCarthy J. Experimental assessment of diesel engine cylinder deactivation performance during low-load transient operations. International Journal of Engine Research. 2021 Feb;22(2):606–15. https://doi.org/10.1177/1468087419857597
  11. Felayati FM, Semin S, Cahyono B, Bakar RA. Numerical Investigation of Dual-Fuel Engine Improvements Using Split Injection Natural Gas Coupled with Diesel Injection Timings at Low Load Condition. International Journal on Engineering Application (IREA). 2021;9(1):31–8. https://doi.org/10.15866/irea.v9i1.19622
  12. Pham Q, Park S, Agarwal AK, Park S. Review of dual-fuel combustion in the compression-ignition engine: Spray, combustion, and emission. Energy. 2022 Jul;250:123778. https://doi.org/10.1016/j.energy.2022.123778
  13. Kumar BA, Selvaraj R, Chelliah TR, Ramesh US. Improved Fuel-Use Efficiency in Diesel–Electric Tugboats With an Asynchronous Power Generating Unit. IEEE Transactions on Transportation Electrification. 2019 Jun;5(2):565–78. https://doi.org/10.1109/TTE.2019.2906587
  14. Iwanski G, Bigorajski Ł, Koczara W. Speed control with incremental algorithm of minimum fuel consumption tracking for variable speed diesel generator. Energy conversion and management. 2018;161:182–92. https://doi.org/10.1016/j.enconman.2018.01.053
  15. Greig M, Wang J. Fuel consumption minimization of variable-speed wound rotor diesel generators. In: IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society [Internet]. IEEE; 2017. p. 8572–7. http://dx.doi.org/10.1109/iecon.2017.8217506
  16. Zhou Y, Liu Q, Wang M, Zhang C. Design of the control circuit used in variable speed diesel generator set. In: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE; 2015. p. 676–80. https://doi.org/10.1109/IAEAC.2015.7428640
  17. Fogli GA, de Almeida PM, Barbosa PG. Modelling and control of an interface power converter for the operation of small diesel gen-sets in grid-connected and stand-alone modes. Electric Power Systems Research. 2017 Sep 1;150:177–87. https://doi.org/10.1016/j.epsr.2017.05.016
  18. Korobko G, Khvatov O, Korobko I. Designing and modelling of variable speed diesel generators for ship unified electric power systems. Vestnik Ivanovskogo gosudarstvennogo energeticheskogo universiteta. 2017;1:55–61. http://dx.doi.org/10.17588/2072-2672.2017.1.055-061
  19. Park K, Kim J. A Study on the Development of a Variable Speed Diesel Generator for DC Distribution. Journal of the Korean Society of Marine Environment and Safety. 2019 Mar 31;25(1):117–21.
  20. Muhtadi MZZ, Soedibyo, Ashari M. Enhancing Diesel Generator Fuel Efficiency by Operating in Optimum Variable-Speed Combined with Vienna Converter. IREA. 2022;10(4).
  21. Kochenderfer MJ, Wheeler TA. Algorithms for optimization. Mit Press; 2019.
  22. Hiroyasu T, Miki M, Kamiura J, Watanabe S, Hiroyasu H. Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model. In 2002 [cited 2023 May 9]. p. 2002-01–2778.
  23. Grudniewski PA, Sobey AJ. Do general Genetic Algorithms provide benefits when solving real problems? In: 2019 IEEE Congress on Evolutionary Computation (CEC) [Internet]. Wellington, New Zealand: IEEE; 2019 [cited 2023 May 8]. p. 1822–9. https://ieeexplore.ieee.org/document/8789997/
  24. Naqvi FB, Shad MY. A new fitness-based selection operator for genetic algorithms to maintain the equilibrium of selection pressure and population. Croat oper res rev (Online). 2022 Jul 12;13(1):113–30.
  25. Ding Y, Zhang W, Yu L, Lu K. The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis. Energy. 2019 Jun;176:582–8. https://doi.org/10.1016/j.energy.2019.04.030
  26. Nagra AA, Han F, Ling QH. An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search. Engineering Optimization. 2019 Jul 3;51(7):1115–32. https://doi.org/10.1080/0305215X.2018.1525709
  27. Li M, Chen H, Wang X, Zhong N, Lu S. An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights. International Journal of Information Technology & Decision Making. 2019 May;18(03):833–66. https://doi.org/10.1142/S0219622019500147
  28. Muhtadi MZZ, Soedibyo, Ashari M. Penetration of Photovoltaic–Synchronous Diesel Generator Systems without Storage for Isolated Area. In: International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE). Jember, Indonesia: IEEE; 2019. p. 227–31. https://doi.org/10.1109/ICOMITEE.2019.8921203
  29. Cuculić A, Ćelić J, Prenc R. Marine diesel-generator model for voltage and frequency variation analysis during fault scenarios. Pomorski zbornik. 2016;51(1):11–24. https://doi.org/10.18048/2016.51.01
  30. IEEE Std 421.5. IEEE recommended practice for excitation system models for power system stability studies (IEEE Std 421.5-2005). Energy Development and Power Generating Committee of the Power Engineering Society. 2005;
  31. Nannam HC, Banerjee A. A Detailed Modeling and Comparative Analysis of Hysteresis Current Controlled Vienna Rectifier and Space Vector Pulse Width Modulated Vienna Rectifier in Mitigating the Harmonic Distortion on the Input Mains. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) [Internet]. Bangkok: IEEE; 2018 [cited 2021 May 19]. p. 371–5. https://ieeexplore.ieee.org/document/8607388/
  32. Reddy D, Ramasamy S. Design of RBFN Controller Based Boost Type Vienna Rectifier for Grid-Tied Wind Energy Conversion System. IEEE Access. 2018;6:3167–75. https://doi.org/10.1109/ACCESS.2017.2787567
  33. Nikouei M. Design and evaluation of the vienna rectifier for a 5MW wind turbine system (Master’s thesis). Chalmers University of Technology; 2013.
  34. Ramasamy S, Reddy D. Design of a three-phase boost type vienna rectifier for 1kW wind energy conversion system. International Journal of Renewable Energy Research (IJRER). 2017;7(4):1909–18. https://doi.org/10.20508/ijrer.v7i4.6306.g7234
  35. Cheema KM, Chaudhary NI, Tahir MF, Mehmood K, Mudassir M, Kamran M, et al. Virtual synchronous generator: Modifications, stability assessment and future applications. Energy Reports. 2022 Nov;8:1704–17. https://doi.org/10.1016/j.egyr.2021.12.064
  36. Hadavi S, Me SP, Bahrani B, Fard M, Zadeh A. Virtual Synchronous Generator Versus Synchronous Condensers: An Electromagnetic Transient Simulation- based Comparison. Cigre Science & Engineering. 2022;024.
  37. Abou-Hussein WM, Dabour SherifM, Hamad MS, Rashad EM. Model Predictive Control for Three-Phase Split-Source Inverter-Based Virtual Synchronous Generator. In: 2021 22nd International Middle East Power Systems Conference (MEPCON) [Internet]. Assiut, Egypt: IEEE; 2021 [cited 2023 Jun 24]. p. 648–53. https://ieeexplore.ieee.org/document/9686236/
  38. DataSheet KTA38-G5. DataSheet Cummins Diesel Engine Product Guide Model KTA38-G5. In Cummins Engine Company, Inc.; 1998.
  39. Stark A. Brake Specific Fuel Consumption (BSFC). X-engineer org: Engineering Tutorials [online]. 2017;
  40. Poli R. Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications. 2008 Feb 14;2008:1–10. https://doi.org/10.1155/2008/685175
  41. Tuegeh M, Soeprijanto S, Purnomo MH. Modified improved particle swarm optimization for optimal generator scheduling. In 2009.
  42. Streicher JT. Applying variable speed drives on a generator power source. energize [Internet]. 2020.
Read More

Author Biographies

M Zaky Zaim Muhtadi, Institut Teknologi Sepuluh Nopember

M. Zaky Zaim Muhtadi received the B.E degree in electrical engineering department from Tanjungpura University, Pontianak, Indonesia, in 2003, and the M.Eng. degree from Gadjah Mada University, Yogyakarta, Indonesia, in 2012. Currently, he is pursuing Ph.D. in the energy conversion system in the Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. His research interests are in cost reduction, diesel-electric generators, energy conversion, hybrid power systems, inverters, photovoltaic power systems, power control, and power generation control.

Heri Suryoatmojo, Institut Teknologi Sepuluh Nopember

Heri Suryoatmojo received his B.S. and M.S. in electrical engineering from Institut Teknologi Sepuluh Nopember (ITS) in 2004, 2006 respectively. After graduated from ITS, he continued doctoral program of electrical engineering in Kumamoto University, Japan and graduated in 2010. His current research interest includes renewable energy applications, wireless power transfer, distributed power system, and electric machines. Dr. Heri Suryoatmojo currently is head of electrical energy conversion laboratory in Institut Teknologi Sepuluh Nopember (ITS), Indonesia.

Soedibyo, Institut Teknologi Sepuluh Nopember

Soedibyo received the Ir., M.MT., and Dr. degress in electrical engineering department from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia, in 1984, 2000, 2013, respectively. He is currently a Professor in the Department of Electrical Engineering, Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember (ITS). His current research interests include renewable energy systems, power electronics, optimization & control of hybrid power systems involving diesel engines, micro-hydro, wind turbine & fuel cell systems, electric machines and drives, intelligent control, and micro-grids.

Mochamad Ashari, Institut Teknologi Sepuluh Nopember

Mochamad Ashari received the M.Eng. and Ph.D. degrees from Curtin University, Australia, in 1997 and 2001, respectively. He is currently a Professor in the Department of Electrical Engineering, Faculty of Intelligent Electrical Engineering and Informatics Technology (Electics), Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. His research interests are in power system operation, power electronics, artificial intelligence in power systems, power system control, optimization of power systems, distributed generation, microgrid simulation, power quality, and renewable energy.

Download this PDF file
PDF
Statistic
Read Counter : 8 Download : 21

Downloads

Download data is not yet available.