QUVVAT TRANSFORMATORLARINI ONLAYN MONITORING VA DIAGNOSTIKASI UCHUN SUN'IY NEYRON TARMOQLARINI QO'LLASH
Kalit so'zlar
https://doi.org/10.47390/ts-v3i10y2025No9Kalit so'zlar
quvvat transformatorlari, neyron tarmoqlari, diagnostika, DGA, bashoratli texnik xizmat ko'rsatish, anomaliyalarni aniqlash.Annotasiya
Sun'iy neyron tarmoqlarini (ANN) qo'llash quvvat transformatorlarining sog'lig'ini monitoring qilish va boshqarishda inqilob qilmoqda, paradigmani rejalashtirilgan profilaktik texnik xizmatdan aqlli holatga asoslangan xizmatga o'tkazmoqda. Ushbu maqolada ANN arxitekturalari va ularni transformator diagnostikasida qo'llash bo'yicha keng qamrovli tadqiqot keltirilgan. Erigan gaz tahlili (DGA), tebranish, SFRA va qisman zaryadsizlanish o'lchovlari kabi ko'p manbali ma'lumotlardan foydalangan holda, tadqiqot neyron tarmoqlarining erta nosozliklarni aniqlash, qoldiq umrni bashorat qilish va texnik xizmat ko'rsatish jadvallarini optimallashtirish imkonini berishini ko'rsatadi. Natijalar an'anaviy chegaraga asoslangan tizimlarga nisbatan diagnostika aniqligi va iqtisodiy samaradorligining sezilarli darajada yaxshilanganligini ko'rsatadi.
Manbalar
1. Kumar, P., & Singh, B. (2023). Deep learning-based health index estimation for power transformers using hybrid autoencoder–LSTM architecture. IEEE Transactions on Power Delivery, 38(4), 2995–3007.
2. Zhao, Y., Chen, H., & Li, X. (2022). Anomaly detection in transformer monitoring data using probabilistic neural networks. Electric Power Systems Research, 211, 108441.
3. Sharma, N., & Das, S. (2021). Bayesian inference and fuzzy logic-based reliability assessment of substation transformers. International Journal of Electrical Power & Energy Systems, 133, 107205.
4. Liu, J., Zhang, W., & Gao, D. (2023). A hybrid SCADA-IoT architecture for intelligent transformer diagnostics. IEEE Access, 11, 107931–107945.
5. Sarkar, A., & Bose, T. (2022). Data-driven prognostics of power transformers using adaptive neuro-fuzzy inference systems. IET Generation, Transmission & Distribution, 16(14), 2793–2804.
6. Kandula, S., & Rajan, R. (2020). Transformer fault diagnosis through dissolved gas analysis using ensemble machine learning models. IEEE Transactions on Dielectrics and Electrical Insulation, 27(6), 1995–2006.
7. Mohanty, S., & Tripathy, M. (2024). Integration of digital twins for real-time condition monitoring of high-voltage transformers. Energies, 17(3), 872.
8. Zhou, K., & Yan, C. (2021). Predictive maintenance of power transformers using long short-term memory networks. Journal of Electrical Engineering & Technology, 16(2), 901–911.
9. Alonso, P., & García, D. (2023). Reliability-centered maintenance optimization for transformers using fuzzy Bayesian networks. Electric Power Components and Systems, 51(7–8), 655–670.
10. Bhattacharya, R., & Patel, D. (2022). Internet of Things-enabled diagnostic framework for online monitoring of power transformers. IEEE Internet of Things Journal, 9(18), 17612–17623.*
This work is licensed under a 