Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

Aidin Shaghaghi, Reza Omidifar, Rahim Zahedi, Ali Asghar Pourezzat, Mansour Keshavarzzadeh

Article ID: 2087
Vol 6, Issue 2, 2023

VIEWS - 61 (Abstract) 22 (PDF)


To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables to the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.


failure rate prediction; seasonal time series models; artificial neural network; electricity distribution company

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1. Keshavarzzadeh M, Zahedi R, Eskandarpanahe R, et al. Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm. Heliyon 2023; 9(4): e15304. doi: 10.1016/j.heliyon.2023.e15304

2. Romanov OI, Nesterenko M, Mankivskyi V. The method of redistributing traffic in mobile network. In: Data-Centric Business and Applications. Springer; 2021. pp. 159–182.

3. Farshchian G, Darestani SA, Hamidi N. Developing a decision-making dashboard for power losses attributes of Iran’s electricity distribution network. Energy 2021; 216: 119248. doi: 10.1016/

4. Zare P, Ghadimi H, Zare R, et al. The study impact of restructuring on efficiency of iran’s electricity distribution and transmission network. In: Proceedings of the 2023 8th International Conference on Technology and Energy Management (ICTEM); 8–9 February 2023; Iran.

5. Brown RE. In: Electric Power Distribution Reliability, 2nd ed. CRC Press; 2017.

6. Fuchs EF, Masoum MAS. Power Quality In Power Systems And Electrical Machines, 1st ed. Academic Press; 2011.

7. Safari A, Das N, Langhelle O, et al. Natural gas: A transition fuel for sustainable energy system transformation? Energy Science & Engineering 2019; 7(4): 1075–1094. doi: 10.1002/ese3.380

8. Estelaji F, Naseri A, Zahedi R. Evaluation of the performance of vital services in urban crisis management. Advances in Environmental and Engineering Research 2022; 3(4): 1–19. doi: 10.21926/aeer.2204057

9. Zahedi R, Ghodusinejad MH, Gitifar S. Threats evaluation of border power plants from the perspective of fuel type and providing solutions to deal with them: a case study of Iran. Transactions of the Indian National Academy of Engineering 2022; 8(1): 1–13. doi: 10.1007/s41403-022-00377-5

10. Plieva M, Gavrina O, Kabisov A. Analysis of technological damage at 110 kV substations in JSC IDGC of the north caucasus-«Sevkavkazenergo». In: Proccedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon); 1–4 October 2019; Vladivostok, Russian.

11. Dehghanian P, Fotuhi-Firuzabad M, Aminifar F, Billinton R. A comprehensive scheme for reliability centered maintenance in power distribution systems—Part I: Methodology. IEEE Transactions on Power Delivery 2013; 28(2): 761–770. doi: 10.1109/TPWRD.2012.2227832

12. Richter L, Lehan M, Marchand S, et al. Artificial intelligence for electricity supply chain automation. Renewable and Sustainable Energy Reviews 2022; 163: 112459. doi: 10.1016/j.rser.2022.112459

13. Rojek I, Studzinski J. Detection and localization of water leaks in water nets supported by an ICT system with artificial intelligence methods as a way forward for smart cities. Sustainability 2019; 11(2): 518. doi: 10.3390/su11020518

14. Shi J, Guo J, Zheng S. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews 2012; 16(5): 3471–3480. doi: 10.1016/j.rser.2012.02.044

15. Samui P, Sekhar S, Balas VE. Handbook of neural computation, 1st ed. CRC Press; 2017.

16. Cherif K, Yahia N, Bilal B, Bilal B. Erosion potential model-based ANN-MLP for the spatiotemporal modeling of soil erosion in wadi saida watershed. Modeling Earth Systems and Environment 2023; pp. 1–23. doi: 10.1007/s40808-022-01657-3

17. Estelaji F, Aghajari AA, Zahedi R. Flood zoning and developing strategies to increase resilience against floods with a crisis management approach. Asian Review of Environmental and Earth Sciences 2023; 10(1): 14–27. doi: 10.20448/arees.v10i1.4439

18. Khah MV, Zahedi R, Eskandarpanah R, et al. Optimal sizing of residential photovoltaic and battery system connected to the power grid based on the cost of energy and peak load. Heliyon 2023; 9(3): e14414. doi: 10.1016/j.heliyon.2023.e14414

19. Fallahi S, Shaverdi M, Bashiri V. Applying GMDH-type neural network and genetic algorithm for stock price prediction of Iranian cement sector. Applications and Applied Mathematics: An International Journal (AAM) 2011; 6(2): 572–591.

20. Chen Q, Guo H, Zheng K, Wang Y. Data Analytics in Power Markets. Springer; 2021.

21. Zahedi R, Eskandarpanah R, Akbari M, et al. Development of a new simulation model for the reservoir hydropower generation. Water Resources Management 2022; 36(7): 1–16. doi: 10.1007/s11269-022-03138-9

22. Madsen H. Time Series Analysis. CRC Press; 2007.

23. Harvey AC, Todd PHJ. Forecasting economic time series with structural and Box-Jenkins models: A case study. Journal of Business & Economic Statistics 1983; 1(4): 299–307. doi: 10.2307/1391661

24. Zahedi R, Daneshgar S, Golivari S. Simulation and optimization of electricity generation by waste to energy unit in Tehran. Sustainable Energy Technologies and Assessments 2022; 53: 102338. doi: 10.1016/j.seta.2022.102338

25. Gupta M, Jin L, Homma N. Static And Dynamic Neural Networks: From Fundamentals To Advanced Theory, 1st ed. Wiley-IEEE Press; 2003.

26. Guo R, Lin Z, Shan T, et al. Physics embedded deep neural network for solving full-wave inverse scattering problems. IEEE Transactions on Antennas and Propagation 2021; 70(8): 6148–6159. doi: 10.1109/TAP.2021.3102135

27. Elakkiya E, Selvakumar S. Stratified hyperparameters optimization of feed-forward neural network for social network spam detection (SON2S). Soft Computing 2022; 26(2): 11915–11934. doi: 10.1007/s00500-022-07020-z



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