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Journals / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2020 / Cilt: 16 - Sayı: 3

Real-Time Prediction of Electricity Distribution Network Status Using Artificial Neural Network Model: A Case Study in Salihli (Manisa, Turkey)

Mahmut SAYAR
Hilmi YÜKSEL
Pages
307–321
Publication Date
2020-09-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is aimed to effective risk management and make anticipation of power outage occurring in electricity transmission network, to make preventive maintenance for failures, to make suggestions for early intervention and shortening downtime and maintenance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electricity distribution networks are critical to the delivery of energy and the continuity of the economy.
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Key Findings & Data:The healthy and efficient operation of these networks depends on the prediction of failures, their early detection and the rapid recovery of the resulting failures.
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Conclusion & Impact:It is aimed to effective risk management and make anticipation of power outage occurring in electricity transmission network, to make preventive maintenance for failures, to make suggestions for early intervention and shortening downtime and maintenance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Production and Service Systems · Operations Management · Artificial Intelligence

Abstract

Electricity distribution networks are critical to the delivery of energy and the continuity of the economy. The healthy and efficient operation of these networks depends on the prediction of failures, their early detection and the rapid recovery of the resulting failures. The causes of failure are internal and external factors. Many studies in different sectors that use different techniques for failure prediction in the literature. The use of artificial intelligence techniques, which are becoming increasingly important today, in failure estimates; in terms of estimation success and effectiveness, it brings many privileges compared to other techniques. In this study, a status prediction model has been developed by using artificial neural network (ANN) technique for power outages and healthy working conditions of the electricity distribution network installed in Salihli district of Manisa province. In previous studies, using artificial intelligence techniques in the energy sector generally focused on one component of network, lifetime, energy demand estimation, battery life and goods failures. The effect of meteorological factors has not been studied on the distribution network situation using artificial intelligence techniques. In this study we use hourly power outages and hourly meteorological factors that cause failures or healthy conditions. It is aimed to effective risk management and make anticipation of power outage occurring in electricity transmission network, to make preventive maintenance for failures, to make suggestions for early intervention and shortening downtime and maintenance.

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Cite This Article

SAYAR, M., YÜKSEL, H. (2020). Real-Time Prediction of Electricity Distribution Network Status Using Artificial Neural Network Model: A Case Study in Salihli (Manisa, Turkey). Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 16 - Sayı: 3, 307–321. https://ajindex.com/articles/88591

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References (12)

  1. Lee, J., Davari, H., Singh, J. ve Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. doi:10.1016/j.mfglet.2018.09.002
  2. Hecht-Nielsen, R. (1990). Neurocomputing, Addison. Wesely Publishing Company. Hornik, K. Stinchcombe, M. White, H.(1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2(359366), 3168–3176
  3. Basheer, I. A. ve Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of microbiological methods, 43(1), 3–31
  4. Shaban, S. E., Hazzaa, M. H. ve El-Tayebany, R. A. (2019). Applying Monte Carlo and artificial intelligence techniques for 235U mass prediction in samples with different enrichments. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 916, 322–326. doi:10.1016/j.nima.2018.10.008
  5. Marugán, A. P., Márquez, F. P. G., Perez, J. M. P. ve Ruiz-Hernández, D. (2018). A survey of artificial neural network in wind energy systems. Applied energy, 228, 1822–1836
  6. Kruse, R., Borgelt, C., Braune, C., Mostaghim, S. ve Steinbrecher, M. (2016). Multilayer perceptrons. Computational Intelligence içinde (ss. 47–92). Springer
  7. Zhang, J. ve Li, J. (2020). Testing and verification of neural-network-based safety-critical control software: A systematic literature review. Information and Software Technology, 106296
  8. Anderson, D. ve McNeill, G. (1992). Artificial neural networks technology. Kaman Sciences Corporation, 258(6), 1–83
  9. Zhang, S., Zhai, B., Guo, X., Wang, K., Peng, N. ve Zhang, X. (2019). Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. Journal of Energy Storage, 26, 100951
  10. Gardner, M. W. ve Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14–15), 2627–2636
  11. Lu, Y., Sun, L., Zhang, X., Feng, F., Kang, J. ve Fu, G. (2018). Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Applied Ocean Research, 74, 69–79
  12. Berecibar, M., Devriendt, F., Dubarry, M., Villarreal, I., Omar, N., Verbeke, W. ve Van Mierlo, J. (2016). Online state of health estimation on NMC cells based on predictive analyti