[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Social Network Membership
Linkedin
Researchgate
..
Indexing Databases
..
DOI
کلیک کنید
..
ِDOR
..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 8, Issue 1 (9-2019) ::
ieijqp 2019, 8(1): 53-61 Back to browse issues page
Optimal Modeling and Forecasting of Equipment Failure Rate for the Electricity Distribution Network
Shervin Asadzadeh *
Department of Industrial Engineering, North Tehran Branch, Islamic Azad University
Abstract:   (3903 Views)
In order to gain a deep understanding of planned maintenance, check the weaknesses of distribution network and detect unusual events, the network outage should be traced and monitored. On the other hand, the most important task of electric power distribution companies is to supply reliable and stable electricity with the minimum outage and standard voltage. This research intends to use time series and artificial neural network and propose some models to forecast the failure rate of equipment in the two regions controlled by Tehran Power Distribution Company. The data have been extracted weekly from the ENOX software from March 2012 to March 2016. To this end, after data pre-processing, the appropriate models have been provided using Minitab and MATLAB software. Moreover, the average air temperature, the average rainfall and the average wind speed were selected as inputs to the neural network. The mean square error (MSE) was used as a criterion to evaluate the error corresponding to the proposed models. The results revealed that time series models perform better than MLP neural network in forecasting equipment failure rates and thus they can be used for future periods.
 
Keywords: Failure Rate Forecasting, Seasonal Time Series Models (SARIMA), Artificial Neural Network, Electricity Distribution Company
Full-Text [PDF 1150 kb]   (800 Downloads)    
Type of Study: Applicable |
Received: 2019/02/9 | Accepted: 2019/05/19 | Published: 2019/08/27
References
1. [1] Quiroga, O. A., Meléndez, J., Herraiz, S. "Fault Causes Analysis in Feeders of Power Distribution Networks", Renewable Energies and Power Quality Journal, Vol. 1, No. 5, pp. 1269-1272, 2011. [DOI:10.24084/repqj09.619]
2. [2] Kaigui, X., Hua, Zh., Chanan, S. "Reliability Forecasting Models for Electrical Distribution Systems Considering Component Failures and Planned Outages", Internationl Journal of Electrical Power and Energy Systems, Vol.79, pp. 228-234,2016. [DOI:10.1016/j.ijepes.2016.01.020]
3. [3] Tong, L., Liang, Y. "Forecasting Field Failure Data for Repairable Systems Using Neural Networks and SARIMA Model", International Journal of Quality and Reliability Management, Vol. 22, No. 4, pp. 410-420, 2016. [DOI:10.1108/02656710510591237]
4. [4] Kutyłowska, M. "Neural Network Approach for Failure Rate Prediction", Engineering Failure Analysis, Vol. 47, pp.41-48, 2015. [DOI:10.1016/j.engfailanal.2014.10.007]
5. [5] Jinxing, C., Jianzhou, W. "Short-term Electricity Prices Forecasting Based on Support Vector Regression and Auto-Regressive Integrated Moving Average Modeling". Energy Conversion and Management, Vol.51, Issue.11, pp.1911-1917, 2010. [DOI:10.1016/j.enconman.2010.02.023]
6. [6] Azadeh, A., Tasaoudani, B., Anvarian, N., saberi, M. "An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electricity Consumption Forecasting (2008-2015):A Case Study of the Group of Eight (G8). The 14th Asia Pacific Regional Meeting of International Foundation for Production Research, 2010.
7. [7] صادقی.ح.، افضلیان.ع.، حقانی.م.، سهرابی وفا.ح. "پیش بینی تقاضای بلندمدت انرژی الکتریکی با استفاده از الگوریتم ترکیبی عصبی-فازی و انبوه ذرات"، تحقیقات مدلسازی اقتصادی، شماره10، ص 21-56، 1391.
8. [8] سرورطاهرآبادی م.، قره پتیان گ.، فریدونیان ع. "دسته بندی و تحلیل عوامل خطا بر اساس تکنیک خوشه بندی در شبکه توزیع برق"، بیستمین کنفرانس توزیع بر، زاهدان، 1394.
9. [9] پروین نیا. الف.، فرداد .خ " 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 ،جلد 7، شماره13، ص 15-27، 1397.
10. [10] استادی، ب.، معتمدی. الف.، حسین زاده کاشان.، ع، امین ناصری، م.، "پیشبینی قیمت تسویه بازار برای خوشه های زمانی رقابت پذیری بازار با استفاده از شبکه عصبی بهبود یافته با الگوریتم ژنتیک: مطالعه بازار برق ایران "، نشریه کیفیت و بهره وری صنعت برق ایران ،جلد 7، شماره13، ص 84-92، 1397.
11. [11] Elamin, N., Fukushige, M. "Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions", Energy, Vol. 165, No. B, pp. 257-268, 2018. [DOI:10.1016/j.energy.2018.09.157]
12. [12] وی، و.، تحلیل سری های زمانی روش‌های یک متغیری و چند متغیری. ترجمه نیرومند، ح. انتشارات دانشگاه فردوسی مشهد، 1386.
13. [13] منهاج، م. مباني شبكه¬هاي عصبي، ، انتشارات دانشگاه صنعتی امیرکبیر، 1396


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Asadzadeh S. Optimal Modeling and Forecasting of Equipment Failure Rate for the Electricity Distribution Network . ieijqp 2019; 8 (1) :53-61
URL: http://ieijqp.ir/article-1-607-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 8, Issue 1 (9-2019) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
Persian site map - English site map - Created in 0.05 seconds with 40 queries by YEKTAWEB 4645