|
Improving the accuracy of forecasting unplanned power extinction (outage) time of power distribution network using ARIMAX time series model (Case study: Yazd power distribution network)
|
Elham Fallah Baghmortini1    , Motahareh Karami1 , Davood Shishebori * 1 |
| 1- Yazd University |
|
|
Abstract: (1423 Views) |
| Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year |
|
| Keywords: Power distribution network, forecasting, unplanned blackouts (outage), time series, energy |
|
|
Full-Text [PDF 909 kb]
(244 Downloads)
|
Type of Study: Research |
Received: 2022/04/6 | Accepted: 2022/10/10 | Published: 2022/11/1
|
|
|
|
|
|
|
| References |
1. Ahmed, A. I., McLeod, R. S., & Gustin, M. (2021). Forecasting underheating in dwellings to detect excess winter mortality risks using time series models. Applied Energy, 286, 116517. [ DOI:10.1016/j.apenergy.2021.116517] 2. Arjmand, A., Samizadeh, R., & Saryazdi, M. D. (2019). Improved Forecasting of Short Term Electricity Demand by using of Integrated Data Preparation and Input Selection Methods. Journal of Energy Management and Technology, 3(1), 48-57. [ DOI:10.22109/JEMT.2018.126045.1077] 3. Armano, G., & Pegoraro, P. A. (2022). Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads. Energies, 15(2), 549. [ DOI:10.3390/en15020549] 4. Collino, E., & Ronzio, D. (2021). Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System. Energies, 14(3), 789. [ DOI:10.3390/en14030789] 5. Ding, S., Hipel, K. W., & Dang, Y. G. (2018). Forecasting China's electricity consumption using a new grey prediction model. Energy, 149. [ DOI:10.1016/j.energy.2018.01.169] 6. Elamin, N., & Fukushige, M. (2018). Modeling and forecasting hourly electricity demand by SARIMAX with interactions. Energy, 165, 257-268. [ DOI:10.1016/j.energy.2018.09.157] 7. G, S. G. N., & Sheshadri, G. S. (2019). ARIMAX Model for Short-Term Electrical Load Forecasting. International Journal of Recent Technology and Engineering, 8(4), 2786-2790. [ DOI:10.35940/ijrte.D7950.118419] 8. Gupta, A., Chawla, M., & Tiwari, N. (2022). Electricity Power Consumption Forecasting Techniques: A survey. SSRN Electronic Journal. [ DOI:10.2139/ssrn.4019431] 9. Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41-48. [ DOI:10.1016/j.engfailanal.2014.10.007] 10. Liu, H., & Shi, J. (2013). Applying ARMA-GARCH approaches to forecasting short-term electricity prices. Energy Economics, 37. [ DOI:10.1016/j.eneco.2013.02.006] 11. Quiroga, O. A., Meléndez, J., & Herraiz, S. (2011). Fault causes analysis in feeders of power distribution networks. Renewable Energy and Power Quality Journal, 1269-1272. [ DOI:10.24084/repqj09.619] 12. Rabbi, F., Tareq, S. U., Islam, M. M., Chowdhury, M. A., & Abul Kashem, M. (2020). A Multivariate Time Series Approach for Forecasting of Electricity Demand in Bangladesh Using ARIMAX Model. 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), 1-5. [ DOI:10.1109/STI50764.2020.9350326] 13. Souto, L., Meléndez, J., & Herraiz, S. (2021). Monitoring of low voltage grids with multilayer principal component analysis. International Journal of Electrical Power & Energy Systems, 125, 106471.
https://doi.org/10.1016/j.ijepes.2020.106471 [ DOI:https://doi.org/10.1016/j.ijepes.2020.106] 14. Xie, K., Zhang, H., & Singh, C. (2016). Reliability forecasting models for electrical distribution systems considering component failures and planned outages. International Journal of Electrical Power & Energy Systems, 79, 228-234. [ DOI:10.1016/j.ijepes.2016.01.020] 15. Zolfaghari, M., Sahabi, B., & Faghihian, F. (2022). The Electricity Consumption Forecast: Adopting a Hybrid Approach by Deep Learning and Arimax-Garch Models. SSRN Electronic Journal. [ DOI:10.2139/ssrn.4031543] 16. اسدزاده، ش، (1398). مدلسازی و پیشبینی بهینه نرخ خرابی تجهیزات شبکه توزیع برق . نشریه کیفیت و بهره وری صنعت برق ایران. ۸ (۱) :۶۱-53 17. اکبری، ا، (1399). بازآرایی بهینه شبکه های توزیع با هدف کاهش تلفات، افزایش قابلیت اطمینان و بهبود پروفیل ولتاژ با استفاده از الگوریتم کلونی موشهای وحشی. نشریه علمی-تخصصی تحقیقات نوین در برق. ۹ (۱) :۴۵-۳۵ 18. پروین نیا، ا؛ فرداد، خ، (1397). ارائه یک سیستم تصمیم یار جهت پیش بینی خاموشی اضطراری نیروگاه های برق آبی با استفاده از استخراج قوانین انجمنی مطالعه موردی: نیروگاه برق آبی مارون بهبهان. نشریه کیفیت و بهره وری صنعت برق ایران. ۷ (۱) :۲۷-۱۵ 19. سرورطاهرآبادی، م؛ قره پتیان، گ؛ فریدونیان، ع،(1394). دسته بندی و تحلیل عوامل خطا بر اساس تکنیک خوشه بندی در شبکه توزیع برق، بیستمین کنفرانس توزیع برق،زاهدان. 20. کرم سلطانی، م؛ مصطفایی، ا؛ یارمحمدی، مسعود، (1391). بررسی تأثیر نوسانات قیمت طلا روی نوسانات قیمت نفت با استفاده از مدل ARIMAX، اولین همایش بین المللی اقتصاد سنجی، روشها و کاربردها، سنندج 21. کریم آبادی، ع؛ حاجی آبادی، م؛ کامیاب، ع، (1395). مروری بر تعمیرات و خرابیهای تجهیزات پستهای انتقال و فوق توزیع. نشریه علمی-تخصصی تحقیقات نوین در برق. ۵ (۲) :۳۱20- 22. هنرمند، م؛ حقی فام، م؛ قاضی زاده، م، (1394). تاثیر فرآیندهای ورود تجهیز در قابلیت اطمینان شبکههای توزیع برق. نشریه کیفیت و بهره وری صنعت برق ایران. ۴ (۱) :۲۳-۱۴
|
|
Fallah Baghmortini E, Karami M, Shishebori D. Improving the accuracy of forecasting unplanned power extinction (outage) time of power distribution network using ARIMAX time series model (Case study: Yazd power distribution network). ieijqp 2022; 11 (4) :39-47 URL: http://ieijqp.ir/article-1-893-en.html
|
