AQLLI ENERGETIKA TIZIMLARIDA SVM ALGORITMI ASOSIDA AVTOMATLASHTIRISHNING TEXNOLOGIK AFZALLIKLARI
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Kalit so‘zlar: Aqlli energiya tizimi, SVM, MEUT(AC), SCADA, avtomatlashtirish, sun’iy intellekt, nosozlik aniqlash, texnik xizmat, optimal joylashtirish.Аннотация
Annotatsiya: Ushbu maqolada aqlli energiya tizimlarida avtomatlashtirishning texnologik afzalliklari va sun’iy intellekt algoritmlaridan biri — Support Vector Machine (SVM) algoritmining qo‘llanilishi tahlil qilinadi. Ayniqsa, MEUT(AC) qurilmalari bilan integratsiyada SVM yordamida nosozliklarni aniqlash, tizim holatini baholash, reaktiv quvvatni boshqarish va optimal joylashtirish kabi jarayonlar samarali tarzda avtomatlashtirilishi yoritiladi.Библиографические ссылки
1. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.
2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
3. Kundur, P. (1994). Power System Stability and Control. McGraw-Hill.
4. Glover, J. D., Sarma, M. S., & Overbye, T. J. (2011). Power System Analysis and Design. Cengage Learning.
5. IEEE Power & Energy Society. Standards on SCADA and PMU technologies.
6. Jabr, R. A., & Pal, B. C. (2009). A flexible AC transmission system (FACTS) controller based on SVM. IEEE Transactions on Power Systems.
Опубликован
2025-05-09
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Articles
Как цитировать
Abraev Tursunpolat Azamat o’g’li, & Samad Nimatov. (2025). AQLLI ENERGETIKA TIZIMLARIDA SVM ALGORITMI ASOSIDA AVTOMATLASHTIRISHNING TEXNOLOGIK AFZALLIKLARI. World Scientific Research Journal, 39(1), 166-172. https://scientific-jl.com/wsrj/article/view/12617
