Fuzzy model of compensation for aging factors of distribution transformers
DOI:
https://doi.org/10.15588/1607-6761-2024-2-1Keywords:
Electric vehicle, transformer’s aging, battery energy storage, photovoltaic systems, power distribution, reactive power compensationAbstract
Purpose. This paper aims to evaluate the negative factors that affect the aging of power distribution transformers, develop a fuzzy control model for their compensation, and study the results of applying the proposed model to different modes of the electrical power network.
Methodology. The mathematical method of fuzzy logic was used to implement the control system of the power grid operating modes.
Findings. The article presents a structure based on fuzzy logic for compensating depreciation factors of distribution transformers. A tuning algorithm and measures were developed to optimize the transformer's load level and power factor. The developed model analyzes the parameters and factors affecting the normal operation of the transformer and warns of dangerous factors that threaten reliability and may lead to a malfunction. In addition, the efficiency of PV generating stations, shunt capacitor banks, and energy storage systems installed on the secondary voltage side to preserve the service life of distribution transformers was analyzed and discussed.
Originality. The paper further develops the fuzzy logic models used to optimize the operation of the power grid and compensate for the aging factors of power distribution transformers
Practical value. The results obtained in the paper can be used to build an optimal system for controlling the operation modes of the electric power grid, which reduces the factors that accelerate the aging of power distribution transformers.
References
Falchetta, G., & Noussan, M. (2021). Electric vehicle charging network in Europe: An accessibility and de-ployment trends analysis. Transportation Research Part D: Transport and Environment, 94, 102813. DOI: 10.1016/j.trd.2021.102813
Diahovchenko, I., Chuprun, A., & Čonka, Z. (2023). Assessment and mitigation of the influence of rising charging demand of electric vehicles on the aging of distribution transformers. Electric Power Systems Re-search, 221, 109455. DOI: 10.1016/j.epsr.2023.109455
Jones, C. B., Lave, M., Vining, W., & Garcia, B. M. (2021). Uncontrolled electric vehicle charging impacts on distribution electric power systems with primarily residential, commercial or industrial loads. Energies, 14(6), 1688. DOI: 10.3390/en14061688
Argade, S., Aravinthan, V., & Jewell, W. (2012, March). Probabilistic modeling of EV charging and its impact on distribution transformer loss of life. In 2012 IEEE International Electric Vehicle Conference (pp. 1-8). IEEE. DOI: 10.1109/IEVC.2012.6183209
Mantilla, H. F. M., Pavas, A., & Durán, I. C. (2017, May). Aging of distribution transformers due to voltage harmonics. In 2017 IEEE Workshop on Power Electronics and Power Quality Applications (PEPQA) (pp. 1-5). IEEE. DOI: 10.1109/PEPQA.2017.7981649
Dao, T., & Phung, B. T. (2018). Effects of voltage harmonic on losses and temperature rise in distribution transformers. IET Generation, Transmission & Distribution, 12(2), 347-354. DOI: 10.1049/iet-gtd.2017.0498
Paevere, P., Higgins, A., Ren, Z., Horn, M., Grozev, G., & McNamara, C. (2014). Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load. Sustainability science, 9, 61-76. DOI: 10.1007/s11625-013-0235-3
Visakh, A., & Selvan, M. P. (2022). Smart charging of electric vehicles to minimize the cost of chargingand the rate of transformer aging in a residential distribution network. Turkish Journal of Electrical Engineering and Computer Sciences, 30(3), 927-942. DOI: 10.55730/1300-0632.3819
Brinkel, N. B. G., Schram, W. L., AlSkaif, T. A., Lampropoulos, I., & Van Sark, W. G. J. H. M. (2020). Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits. Applied Energy, 276, 115285. DOI: 10.1016/j.apenergy.2020.115285
Nafisi, H. (2021). Investigation on distribution transformer loss-of-life due to plug-in hybrid electric vehicles charging. International Journal of Ambient Energy, 42(7), 744-750. DOI: 10.1080/01430750.2018.1563816
Palomino, A., & Parvania, M. (2020). Data-driven risk analysis of joint electric vehicle and solar operation in distribution networks. IEEE Open Access Journal of Power and Energy, 7, 141-150. DOI: 10.1109/OAJPE.2020.2984696
Affonso, C. M., & Kezunovic, M. (2018, June). Probabilistic assessment of electric vehicle charging demand impact on residential distribution transformer aging. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (pp. 1-6). IEEE. DOI: 10.1109/PMAPS.2018.8440211
Romero Quete, A. A., Mombello, E. E., & Rattá, G. (2016). Evaluación de la pérdida de vida del aislamiento solido en transformadores de potencia, estimando la historia de carga y los perfiles de temperatura ambiente por medio de redes neuronales artificiales y simulaciones de Monte Carlo. In DYNA (Vol. 83, Issue 197, p. 104). Universidad Nacional de Colombia. DOU: 10.15446/dyna.v83n197.48134
He, L., Li, L., Li, M., Li, Z., & Wang, X. (2022). A Deep Learning Approach to the Transformer Life Prediction Considering Diverse Aging Factors. In Frontiers in Energy Research (Vol. 10). Frontiers Media SA. DOI: 10.3389/fenrg.2022.930093
El-Bataway, S. A., & Morsi, W. G. (2019). Distribution Transformer’s Loss of Life Considering Residential Prosumers Owning Solar Shingles, High-Power Fast Chargers and Second-Generation Battery Energy Storage. In IEEE Transactions on Industrial Informatics (Vol. 15, Issue 3, pp. 1287–1297). Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/tii.2018.2845416
Diahovchenko, I., Petrichenko, R., Petrichenko, L., Mahnitko, A., Korzh, P., Kolcun, M., & Čonka, Z. (2022). Mitigation of transformers’ loss of life in power distribution networks with high penetration of electric vehicles. In Results in Engineering (Vol. 15, p. 100592). Elsevier BV. DOI: 10.1016/j.rineng.2022.100592
Schreider, Yu. A., Tee, G. J., & Henney, A. G. (1967). The Monte Carlo Method: The Method of Statistical Trials. In Physics Today (Vol. 20, Issue 1, pp. 129–129). AIP Publishing. DOI: 10.1063/1.3034116
Shevchenko, S. Yu., Volokhin, V. V., & Diahovchenko, I. M. (2018). Power quality issues in smart grids with photovoltaic power stations. In En-ergetika (Vol. 63, Issue 4). Lithuanian Academy of Sciences. DOI: 10.6001/energetika.v63i4.3623
Seme, S., Lukač, N., Štumberger, B., & Hadžiselimović, M. (2017). Power quality experi-mental analysis of grid-connected photovoltaic sys-tems in urban distribution networks. In Energy (Vol. 139, pp. 1261–1266). Elsevier BV. DOI: 10.1016/j.energy.2017.05.088
Diahovchenko, I., Dolník, B., Kanálik, M., & Ku-rimský, J. (2021). Contemporary electric energy me-ters testing under simulated nonsinusoidal field con-ditions. In Electrical Engineering (Vol. 104, Issue 2, pp. 1077–1092). Springer Science and Business Me-dia LLC. DOI: 10.1007/s00202-021-01365-8
Volokhin, V. V., Diahovchenko, I. M., & Derevyanko, B. V. (2017). Prospects of nanomateri-als use in current and voltage hall sensors to improve the measurements accuracy and reduce the external impacts. In 2017 IEEE 7th International Conference Nanomaterials: Application & Properties (NAP). 2017 IEEE 7th International Conference “Nanomaterials: Application & Properties” (NAP). IEEE. DOI: 10.1109/nap.2017.8190239
Shevchenko, S., Dovgalyuk, O., Danylchenko, D., Rubanenko, O., Fedorchuk, S., & Potryvai, A. (2021). Accounting For The Effect Of PV Panel Dustiness On System Performance With Correction For Panel Cleaning For Matlab Simulink. In 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON). 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineer-ing (UKRCON). IEEE. DOI: 10.1109/ukrcon53503.2021.9575747
Fedorchuk, S., Ivakhnov, A., Bulhakov, O., & Danylchenko, D. (2020). Optimization of Storage Systems According to the Criterion of Minimizing the Cost of Electricity for Balancing Renewable Energy Sources. In 2020 IEEE KhPI Week on Advanced Technology (KhPIWeek). 2020 IEEE KhPI Week on Advanced Technology (KhPIWeek). DOI: 10.1109/khpiweek51551.2020.9250155
Müller, M., Viernstein, L., Truong, C. N., Eiting, A., Hesse, H. C., Witzmann, R., & Jossen, A. (2017). Evaluation of grid-level adaptability for stationary battery energy storage system applications in Europe. In Journal of Energy Storage (Vol. 9, pp. 1–11). Elsevier BV. DOI: 10.1016/j.est.2016.11.005
Beer, C. (1995). Fuzzy Thinking: The New Science of Fuzzy Logic.Bart Kosko. In The Quarterly Review of Biology (Vol. 70, Issue 2, pp. 210–210). University of Chicago Press. DOI: doi.org/10.1086/418985
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