Biological Interactions in Distribution Networks and Analysis of Weather-Related Failures Using Artificial Neural Networks
Öz
Overhead line faults have a critical impact on the reliability of electrical distribution systems. Literature reviews show that overhead lines are highly susceptible to environmental factors such as weather conditions, vegetation and wildlife. This study presents a data-driven approach to analysing biologically induced outages in overhead distribution lines using Artificial Neural Network (ANN) techniques. The study aims to predict regions where outages are likely to occur using meteorological data. For the analysis, real outage data from an electricity distribution company for the period 2021-2023, together with meteorological data for the same period, were used to model the ANN structure in MATLAB software. The model achieved an accuracy rate of 97% on the test data, demonstrating a high generalisation ability. The results of this study provide valuable insights for electricity distribution companies to better understand biologically induced outage problems, and to develop effective models for predicting such outages.
Anahtar Kelimeler
Overhead Distribution Lines Artificial Neural Networks Meteorological Data Analysis Biological Outages
Kaynakça
- [1] Du, Y., Liu, Y., Wang, X., Fang, J., Sheng, G., & Jiang, X. (2020). Predicting weather-related failure risk in distribution systems using Bayesian neural network. IEEE Transactions on Smart Grid, 12(1), 350-360.
- [2] Das, S., Kankanala, P., & Pahwa, A. (2021). Outage estimation in electric power distribution systems using a neural network ensemble. Energies, 14(16), 4797.
- [3] Zhou, Y., Pahwa, A., & Yang, S. S. (2006). Modeling weather-related failures of overhead distribution lines. IEEE Transactions on power systems, 21(4), 1683-1690.
- [4] Brester, C., Niska, H., Ciszek, R., & Kolehmainen, M. (2020, July). Weather-based fault prediction in electricity networks with artificial neural networks. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
- [5] Sarwat, A. I., Amini, M., Domijan, A., Damnjanovic, A., & Kaleem, F. (2016). Weather-based interruption prediction in the smart grid utilizing chronological data. Journal of Modern Power Systems and Clean Energy, 4(2), 1-8.
- [6] Takata, H., Yanase, M., Waki, T., & Hachino, T. (2002, August). A prediction method of electric power damage by typhoons in Kagoshima via GMDH and NN. In Proceedings of the 41st SICE Annual Conference. SICE 2002. (Vol. 4, pp. 2424-2429). IEEE.
- [7] Li, G., Zhang, P., Luh, P. B., Li, W., Bie, Z., Serna, C., & Zhao, Z. (2013). Risk analysis for distribution systems in the northeast US under wind storms. IEEE Transactions on Power Systems, 29(2), 889-898.
- [8] Liu, Y. (2015, June). Short-term operational reliability evaluation for power systems under extreme weather conditions. In 2015 IEEE Eindhoven PowerTech (pp. 1-5). IEEE.
- [9] Zhu, D., Cheng, D., Broadwater, R. P., & Scirbona, C. (2007). Storm modeling for prediction of power distribution system outages. Electric power systems research, 77(8), 973-979.
- [10] Chen, P. C., & Kezunovic, M. (2016). Fuzzy logic approach to predictive risk analysis in distribution outage management. IEEE Transactions on Smart Grid, 7(6), 2827-2836.
- [11] Nanadikar, A. A., Biradar, V. N., & Sarma, D. S. (2014, September). Improved outage prediction using asset management data and intelligent multiple interruption event handling with fuzzy control during extreme climatic conditions. In 2014 International Conference on Smart Electric Grid (ISEG) (pp. 1-7). IEEE.
- [12] Sahai, S., & Pahwa, A. (2006, June). A probabilistic approach for animal-caused outages in overhead distribution systems. In 2006 9th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS (pp. 1-7).
- [13] Kankanala, P., Pahwa, A., & Das, S. (2015). Estimating animal-related outages on overhead distribution feeders using boosting. IFAC-PapersOnLine, 48(30), 270-275.
- [14] Chawthai, P., Raphisak, P., & Katithummarugs, S. (2023, December). Association Rule Mining for Power Outages Caused by Animals and Vegetation in Electrical Distribution Systems. In Proceedings of the 13th International Conference on Advances in Information Technology (pp. 1-5).
Dağıtım Şebekelerinde Biyolojik Etkileşimler ve Hava Koşullarına Bağlı Arızaların Yapay Sinir Ağları ile İncelenmesi
Öz
Havai dağıtım hatlarında meydana gelen arızalar, elektrik dağıtım sistemlerinin güvenilirliği üzerinde kritik bir etkiye sahiptir. Literatür incelemeleri, havai hatların hava koşulları, ağaçlar ve hayvanlar gibi çevresel faktörlere karşı oldukça hassas olduğunu göstermektedir. Bu çalışma, yapay sinir ağları tekniğini kullanarak havai dağıtım hatlarında biyolojik kaynaklı kesintilerin analizi için veri odaklı bir yaklaşım sunmaktadır. Çalışmada, meteorolojik veriler kullanılarak kesintilerin gerçekleşebileceği bölgelerin tahmin edilmesi hedeflenmiştir. Analizlerde, 2021-2023 yılları arasında bir elektrik dağıtım şirketine ait gerçek kesinti verileri ve aynı döneme ait meteorolojik veriler temel alınarak, yapay sinir ağı (YSA) yapısı Matlab yazılımında modellenmiştir. Test verileri üzerinde elde edilen %97 doğruluk oranı, modelin genelleme kapasitesinin oldukça yüksek olduğunu ortaya koymaktadır. Bu çalışmanın sonuçları, elektrik dağıtım şirketlerine biyolojik kaynaklı kesinti sorunlarını daha iyi anlamaları ve bu kesintileri tahmin etmek için etkili modeller geliştirmeleri konusunda bilgiler sağlayabilir.
Anahtar Kelimeler
Elektrik Dağıtım Hatları Yapay Sinir Ağları Meteorolojik Veri Analizi Biyolojik Kesintiler
Kaynakça
- [1] Du, Y., Liu, Y., Wang, X., Fang, J., Sheng, G., & Jiang, X. (2020). Predicting weather-related failure risk in distribution systems using Bayesian neural network. IEEE Transactions on Smart Grid, 12(1), 350-360.
- [2] Das, S., Kankanala, P., & Pahwa, A. (2021). Outage estimation in electric power distribution systems using a neural network ensemble. Energies, 14(16), 4797.
- [3] Zhou, Y., Pahwa, A., & Yang, S. S. (2006). Modeling weather-related failures of overhead distribution lines. IEEE Transactions on power systems, 21(4), 1683-1690.
- [4] Brester, C., Niska, H., Ciszek, R., & Kolehmainen, M. (2020, July). Weather-based fault prediction in electricity networks with artificial neural networks. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
- [5] Sarwat, A. I., Amini, M., Domijan, A., Damnjanovic, A., & Kaleem, F. (2016). Weather-based interruption prediction in the smart grid utilizing chronological data. Journal of Modern Power Systems and Clean Energy, 4(2), 1-8.
- [6] Takata, H., Yanase, M., Waki, T., & Hachino, T. (2002, August). A prediction method of electric power damage by typhoons in Kagoshima via GMDH and NN. In Proceedings of the 41st SICE Annual Conference. SICE 2002. (Vol. 4, pp. 2424-2429). IEEE.
- [7] Li, G., Zhang, P., Luh, P. B., Li, W., Bie, Z., Serna, C., & Zhao, Z. (2013). Risk analysis for distribution systems in the northeast US under wind storms. IEEE Transactions on Power Systems, 29(2), 889-898.
- [8] Liu, Y. (2015, June). Short-term operational reliability evaluation for power systems under extreme weather conditions. In 2015 IEEE Eindhoven PowerTech (pp. 1-5). IEEE.
- [9] Zhu, D., Cheng, D., Broadwater, R. P., & Scirbona, C. (2007). Storm modeling for prediction of power distribution system outages. Electric power systems research, 77(8), 973-979.
- [10] Chen, P. C., & Kezunovic, M. (2016). Fuzzy logic approach to predictive risk analysis in distribution outage management. IEEE Transactions on Smart Grid, 7(6), 2827-2836.
- [11] Nanadikar, A. A., Biradar, V. N., & Sarma, D. S. (2014, September). Improved outage prediction using asset management data and intelligent multiple interruption event handling with fuzzy control during extreme climatic conditions. In 2014 International Conference on Smart Electric Grid (ISEG) (pp. 1-7). IEEE.
- [12] Sahai, S., & Pahwa, A. (2006, June). A probabilistic approach for animal-caused outages in overhead distribution systems. In 2006 9th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS (pp. 1-7).
- [13] Kankanala, P., Pahwa, A., & Das, S. (2015). Estimating animal-related outages on overhead distribution feeders using boosting. IFAC-PapersOnLine, 48(30), 270-275.
- [14] Chawthai, P., Raphisak, P., & Katithummarugs, S. (2023, December). Association Rule Mining for Power Outages Caused by Animals and Vegetation in Electrical Distribution Systems. In Proceedings of the 13th International Conference on Advances in Information Technology (pp. 1-5).
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 11 Aralık 2024 |
| Kabul Tarihi | 20 Aralık 2024 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 3 Sayı: 1 |
