Kuchlanish nosimmetrikligi ta’sirida ishlaydigan fotoelektrik invertorlar va telekommunikatsiya to‘g‘rilagichlarining qolgan xizmat muddatini fizika asosidagi gradiyent busting yordamida prognozlash: simulyatsiya tadqiqoti
##semicolon##
kuchlanish nosimmetrikligi, qolgan xizmat muddati (RUL), XGBoost, fizika asosidagi mashinaviy o‘rganish, konformal bashorat, ma’lumotlar sizib chiqishi, baholash protokoli, fotoelektrik invertor, telekommunikatsiya elektr ta’minoti, prognozli texnik xizmat ko‘rsatishAbstrak
Uzoq muddatli kuchlanish nosimmetrikligi taqsimlangan obyektlardagi uch fazali uskunalar - tarmoqqa ulangan fotoelektrik (PV) invertorlar va telekommunikatsiya to‘g‘rilagich stansiyalari - xizmat muddatini qo‘shimcha isroflar va tezlashgan issiqlik eskirishi orqali qisqartiradi. Bunday uskunalar uchun ishdan chiqishgacha kuzatilgan ma’lumotlar mavjud emasligi sababli gibrid konveyer baholanadi: jamlanuvchi shikastlanishning fizik modeli degradatsiya trayektoriyalarini hosil qiladi, XGBoost regressori esa qolgan xizmat muddatini (RUL) nosimmetriklik va issiqlik yuklamasi belgilaridan o‘rganadi. Asosiy natija metodologik: ma’lumotlar klasterlashgan (520 obyektdan 20 498 kuzatuv), shu sababli kuzatuv darajasidagi ajratish = 0.812 beradi; to‘liq obyekt-ajratilgan protokolda model 12 ta seed bo‘yicha 0.726 ± 0.033 ga erishadi, fizik shikastlanish indeksini olib tashlash esa 1.03 o‘rniga atigi 0.087 ± 0.016 ga tushadi. Konformal intervallar nominal 80 % o‘rniga 84.3 % qamrov beradi (kvintillar bo‘yicha 61.5–94.5 %); uch darajali balanslash tavsiyasi qatlami modelsiz qoidaning 63.9 % iga nisbatan 91.6 % aniqlikka ( = 0.771) erishadi. Tadqiqotning simulyatsion aylanmaliligi ochiq bayon etiladi; asosiy ko‘chiriladigan xulosa - baholash protokoli natijasidir.
##submission.citations##
GOST 32144-2013. Electric energy. Electromagnetic compatibility of technical equipment. Power quality limits in public power supply systems. Interstate Council for Standardization, Metrology and Certification, 2013.
EN 50160:2010. Voltage characteristics of electricity supplied by public electricity networks. CENELEC, 2010.
IEC 61000-4-30:2015. Electromagnetic compatibility (EMC) — Part 4-30: Testing and measurement techniques — Power quality measurement methods. IEC, 2015.
J. P. G. de Abreu and A. E. Emanuel, “Induction motor thermal aging caused by voltage distortion and imbalance: Loss of useful life and its estimated cost,” IEEE Transactions on Industry Applications, vol. 38, no. 1, pp. 12–20, 2002.
P. Gnaciński, “Windings temperature and loss of life of an induction machine under voltage unbalance combined with over- or undervoltages,” IEEE Transactions on Energy Conversion, vol. 23, no. 2, pp. 363–371, 2008.
V. M. Montsinger, “Loading transformers by temperature,” Transactions of the American Institute of Electrical Engineers, vol. 49, no. 2, pp. 776–790, 1930.
H. Wang and F. Blaabjerg, “Reliability of capacitors for DC-link applications in power electronic converters — An overview,” IEEE Transactions on Industry Applications, vol. 50, no. 5, pp. 3569–3578, 2014.
A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation,” in Proc. International Conference on Prognostics and Health Management (PHM), Denver, CO, 2008, pp. 1–9.
X.-S. Si, W. Wang, C.-H. Hu, and D.-H. Zhou, “Remaining useful life estimation — A review on the statistical data driven approaches,” European Journal of Operational Research, vol. 213, no. 1, pp. 1–14, 2011.
Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machinery health prognostics: A systematic review from data acquisition to RUL prediction,” Mechanical Systems and Signal Processing, vol. 104, pp. 799–834, 2018.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, 2016, pp. 785–794.
L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still outperform deep learning on typical tabular data?,” in Advances in Neural Information Processing Systems 35 (NeurIPS 2022), Datasets and Benchmarks Track, 2022.
R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” Information Fusion, vol. 81, pp. 84–90, 2022.
S. Zheng, K. Ristovski, A. Farahat, and C. Gupta, “Long short-term memory network for remaining useful life estimation,” in Proc. IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, 2017, pp. 88–95.
Y. Romano, E. Patterson, and E. J. Candès, “Conformalized quantile regression,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019, pp. 3538–3548.
V. Vovk, A. Gammerman, and G. Shafer, Algorithmic Learning in a Random World, 2nd ed. Cham, Switzerland: Springer, 2022.
M. A. Miner, “Cumulative damage in fatigue,” Journal of Applied Mechanics, vol. 12, no. 3, pp. A159–A164, 1945.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30 (NeurIPS 2017), 2017, pp. 4765–4774.
ANSI/NEMA MG 1-2024. Motors and Generators, §14.36. National Electrical Manufacturers Association, 2024.
IEC 60034-26:2026. Rotating electrical machines — Part 26: Effects of unbalanced voltages on the performance of three-phase cage induction motors, 2nd ed. IEC, 2026.
H. Wang, P. Davari, H. Wang, D. Kumar, F. Zare, and F. Blaabjerg, “Lifetime estimation of DC-link capacitors in adjustable speed drives under grid voltage unbalances,” IEEE Transactions on Power Electronics, vol. 34, no. 5, pp. 4064–4078, 2019.
IEEE Std C57.91-2025. IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators. IEEE, 2026.
IEC 60216-1:2025. Electrical insulating materials — Thermal endurance properties — Part 1: Ageing procedures and evaluation of test results, 7th ed. IEC, 2025.
M. Arias Chao, C. Kulkarni, K. Goebel, and O. Fink, “Fusing physics-based and deep learning models for prognostics,” Reliability Engineering & System Safety, vol. 217, art. no. 107961, 2022.
M. Arias Chao, C. Kulkarni, K. Goebel, and O. Fink, “Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics,” Data, vol. 6, no. 1, art. no. 5, 2021.
L. Liao and F. Köttig, “Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction,” IEEE Transactions on Reliability, vol. 63, no. 1, pp. 191–207, 2014.
S. Peyghami, P. Palensky, and F. Blaabjerg, “An overview on the reliability of modern power electronic based power systems,” IEEE Open Journal of Power Electronics, vol. 1, pp. 34–50, 2020.