AQLLI ENERGETIKA TIZIMLARIDA SVM ALGORITMI ASOSIDA AVTOMATLASHTIRISHNING TEXNOLOGIK AFZALLIKLARI

Authors

  • Abraev Tursunpolat Azamat o’g’li Author
  • Samad Nimatov Author

Keywords:

Kalit so‘zlar: Aqlli energiya tizimi, SVM, MEUT(AC), SCADA, avtomatlashtirish, sun’iy intellekt, nosozlik aniqlash, texnik xizmat, optimal joylashtirish.

Abstract

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.

References

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.

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Published

2025-05-09

Issue

Vol. 39 No. 1 (2025): World scientific research journal|WSRJ|Volume-39|Issue-1

Section

Articles

How to Cite

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